Relevant bibliographies by topics / Learning preferences

Academic literature on the topic 'Learning preferences'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Learning preferences"

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Ch, Abdul Rashid, Irshad Nabi Sandhu, Muhammad Arif Ali, and Asad Ali Sandhu. "LEARNING PREFERENCES." Professional Medical Journal 22, no. 10 (October 10, 2015): 1351–55. http://dx.doi.org/10.29309/tpmj/2015.22.10.1042.

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Introduction: This study has focused to know the learning preferences amongfaculty considering different methodologies and considering about CME to improve healthcare.Objectives: To identify the gaps in knowledge regarding CME in medical faculty of Lahore andto see the awareness of the CME among them. Study Design: This is a cross-sectional study ofmedical faculty in Lahore. Settings: Three hospitals in Lahore are included from both private/public sectors. Period: It was conducted over a period of 2 months from JULY, 2014 to August,2014 Methods: A questionnaire comprising of 21 questions was distributed at random for datacollection among doctors having done post graduation. Results: Most of the faculty memberswho attended CME found it as useful tool for improving the knowledge and techniques forbetter patient care. Conclusions: Majority teachers are not interested in CME and do notparticipate and ask for some incentives.
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Li, Nan, William Cushing, Subbarao Kambhampati, and Sungwook Yoon. "Learning User Plan Preferences Obfuscated by Feasibility Constraints." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 370–73. http://dx.doi.org/10.1609/icaps.v19i1.13393.

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It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.
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Oh, Jaeho, Mincheol Kim, and Sang-Woo Ban. "Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images." Applied Sciences 10, no. 21 (October 29, 2020): 7641. http://dx.doi.org/10.3390/app10217641.

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In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and Indoor Scene Recognition) that include images with atypical visual characteristics in order to train and infer personal visual preferences. The effectiveness of transfer learning for incremental preference learning was verified by experiments using inter-domain visual datasets with different visual characteristics. Moreover, a gradient class activation mapping (Grad-CAM) approach was applied to the proposed model, providing explanations about personal visual preference possibilities. Experiments showed that the proposed preference-learning model using transfer learning outperformed a preference model not using transfer learning. In terms of the accuracy of preference recognition, the proposed model showed a maximum of about 7.6% improvement for the LFW database and a maximum of about 9.4% improvement for the Indoor Scene Recognition database, compared to the model that did not reflect transfer learning.
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Žnidaršič, Martin, Aljaž Osojnik, Peter Rupnik, and Bernard Ženko. "Improving Effectiveness of a Coaching System through Preference Learning." Technologies 10, no. 1 (January 31, 2022): 24. http://dx.doi.org/10.3390/technologies10010024.

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The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.
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Kuznar, Elaine, Grace Falciglia, Linda Wood, and Judith J. Frankel. "Learning Style Preferences." Journal of Nutrition For the Elderly 10, no. 3 (June 3, 1991): 21–34. http://dx.doi.org/10.1300/j052v10n03_02.

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Black, Joyce M. "Assessing Learning Preferences." Plastic Surgical Nursing 24, no. 2 (April 2004): 68–69. http://dx.doi.org/10.1097/00006527-200404000-00010.

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Sengsouliya, Souksakhone, Sithane Soukhavong, Say Phonekeo, Vanmany Vannasy, Vanthala Souvanxay, and Chanmany Rattanavongsa. "The Effect of Contextual Factor on Learning Styles Preferences of English Majors in Lao Public Universities." Journal of English Language Teaching and Linguistics 6, no. 3 (December 15, 2021): 683. http://dx.doi.org/10.21462/jeltl.v6i3.667.

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<em>This research is a quantitative correlational design, which investigates the English learning styles preferences of English major in Laotian public universities in Lao PDR and tests the effect of contextual factors on the participants’ learning styles preferences. The sample of this research involved 542 university-level students who major in English at a bachelor-degree program in four public universities in Lao PDR. The instrument of the study was Reid’s (1987) Perceptual Learning Style Preference Questionnaire (PLSPQ), which includes six different learning styles (Audio, Visual, Kinesthetic, Tactile, Individual, and Group learning styles). Participants were invited to rate their learning style preference towards the scale. The analysis was conducted based on</em> <em>Reid’s (1995) guide of categorizing preference levels, such as Major, Minor Learning Style Preference, and Negligible. The results indicated that the participants had three major preferences towards Kinesthetic learning (M=41.20), Audio learning (M=39.18), and Tactile learning styles (M=38.14), respectively. The study also found that there are significant differences in English learning styles preferences among students from different institutional contexts. Further research on testing the effect of contextual factors on learners’ learning choice is strongly recommended.</em>
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Ismail, Nadia Nur Afiqah, Tina Abdullah, and Abdul Halim Abdul Raof. "INSIGHTS INTO LEARNING STYLES PREFERENCE OF ENGINEERING UNDERGRADUATES: IMPLICATIONS FOR TEACHING AND LEARNING." Journal of Nusantara Studies (JONUS) 7, no. 1 (January 13, 2022): 390–409. http://dx.doi.org/10.24200/jonus.vol7iss1pp390-409.

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Background and Purpose: Education at higher institutions prepares graduates for the real world. To develop and maintain quality, the focus must not only be on what institutions can offer but also on the learning needs and styles of learners. Despite many studies on engineering learners’ learning styles, limited research has been conducted to compare the learning styles of Engineering and Engineering Education learners. This study was conducted to ascertain the learning style preferences of first-year undergraduates from both groups in a science and technology-driven university in Malaysia. Methodology: This descriptive study consisted of 40 Engineering and 40 Engineering Education learners who attended an English language course at the university. Perceptual Learning Style Preference Questionnaire was adopted as the survey instrument. The data were analysed using self-scoring sheet and Statistical Package for the Social Sciences. Findings: While both groups chose Kinaesthetic as a major learning style preference, the Engineering Education learners also chose Group, Tactile, and Auditory learning styles as their other major preferences. Both groups chose Visual and Individual as their minor preferences. Contributions: The findings extend research demonstrating the significant role of specific disciplines in Engineering to determine the learning style preferences of learners. The findings also provide useful insights that suggest implications for practice and policy. Keywords: Engineering, engineering education, English language, learning styles, teaching and learning. Cite as: Ismail, N. N. A., Abdullah, T., & Abdul Raof, A. H. (2022). Insights into learning styles preference of engineering undergraduates: Implications for teaching and learning. Journal of Nusantara Studies, 7(1) 390-409. http://dx.doi.org/10.24200/jonus.vol7iss1pp390-409
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Ruiz, Luis Miguel, Jose Luis Graupera, Juan Antonio Moreno, and Isabel Rico. "Social Preferences for Learning among Adolescents in Secondary Physical Education." Journal of Teaching in Physical Education 29, no. 1 (January 2010): 3–20. http://dx.doi.org/10.1123/jtpe.29.1.3.

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The purpose of the current study was to explore social interaction preferences for learning in Physical Education (PE) among Spanish secondary students. The sample consists of 6,654 students (3,500 girls and 3,154 boys, aged 12–17 years) from public and private urban and rural schools in two communities in Spain. All participants completed the Graupera/Ruiz Scale of Social Interaction Preferences in PE Learning (GR–SIPPEL) which explores four learning preference dimensions: cooperation, competition, affiliation, and individualism. Results indicated that the ordinal profile of students’ preferences in PE classes was: cooperative (very high preference), competitive and affiliate (high-moderate preference), and individualistic (moderate-low preference). Gender differences emerged: girls were less competitive and individualistic than boys, and slightly more cooperative and affiliate. Weak grade level differences were also observed.
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Mistry, Sajib, Sheik Mohammad Mostakim Fattah, and Athman Bouguettaya. "Sequential Learning-based IaaS Composition." ACM Transactions on the Web 15, no. 3 (July 3, 2021): 1–37. http://dx.doi.org/10.1145/3452332.

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We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.
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Dissertations / Theses on the topic "Learning preferences"

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Paciorek, Albertyna. "Implicit learning of semantic preferences." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/244632.

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The research presented in this PhD dissertation examines the phenomenon of semantic implicit learning, using semantic preferences of novel verbs as a test case. Implicit learning refers to the phenomenon of learning without intending to learn or awareness that one is learning at all. Semantic preference (or selectional preference – as preferred in computational linguistics) is the tendency of a word to co-occur with words sharing similar semantic features. For example, ‘drink’ is typically followed by nouns denoting LIQUID, and the verb ‘chase’ is typically followed by ANIMATE nouns. The material presented here spans across disciplines. It examines a well-documented psychological phenomenon - implicit learning – and applies it in the context of language acquisition, thereby providing insights into both fields. The organisation of this dissertation groups its experiments by their methodology. Chapter 1 provides an overview of the current psychological and linguistic literature. Chapter 2 includes a pen-and-paper study carried out in a classroom environment on Polish learners of English, where awareness is assessed by subjective measures taken at each test question as well as a post-experiment questionnaire. Chapter 3 includes a collection of 5 computer-based experiments based on a false-memory paradigm. After exposure to sentential contexts containing novel verbs, participants are shown to endorse more previously unseen verb-noun pairings that follow the correct semantic preference patterns to the pairings that violate it. The result holds even when participants do not reveal any explicit knowledge of the patterns in the final debriefing. Awareness is additionally assessed using indirect measures examining correlations of confidence judgements with performance. Chapter 4 examines whether implicit learning of novel verb semantic preference patterns is automatic. To this end, a reaction time procedure is developed based on two consecutive decisions (“double decision priming”). The method reveals that semantic implicit learning, at least in the described cases, exerts its influence with a delay, in post-processing. Chapter 5 comprises research done in collaboration with Dr Nitin Williams, University of Reading. It documents an attempt at finding neural indices of implicit learning using a novel single-trial analysis of an electroencephalographic (EEG) signal, based on empirical mode decomposition (EMD) denoising. Chapter 6 presents a final discussion and indications for future research. The main contribution of this dissertation to the general field of implicit learning research consists in its challenging the predominant view that implicit learning mainly relies on similarity of forms presented in training and test. The experiments presented here require participants to make generalisations at a higher, semantic level, which is largely independent of perceptual form. The contribution of this work to the field of Second Language Acquisition consists of empirical support for the currently popular but seldom tested assumptions held by advocates of communicative approaches to language teaching, namely that certain aspects of linguistic knowledge can develop without explicit instruction and explanation. At the same time, it challenges any view assuming that vocabulary learning necessarily relies on explicit mediation. The experiments collected here demonstrate that at least word usage in context can be learnt implicitly. A further contribution of this dissertation is its demonstration that the native language may play a key role in determining what is learnt in such situations. A deeper understanding of the phenomenon of semantic implicit learning promises to shed light on the nature of word and grammar learning in general, which is crucial for an account of the processes involved in the development of a second language mental lexicon.
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Miller, Robert W. "Learning Preferences of Commercial Fishermen." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5532.

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This study surveyed 435 commercial fishermen across eight coastal regions of the United States where commercial fishing takes place. The regions of the study included: Northeast Atlantic, Mid-Atlantic, Southeast Atlantic, Gulf of Mexico, Great Lakes, Southern Pacific, Pacific Northwest, and Alaska. Participants were asked to complete the Commercial Fishing Worker Survey (CFWS), which is a survey instrument consisting of an approved, adapted version of the Index of Learning Styles instrument (ILS) combined with a demographic section which included questions designed to obtain data regarding the four variables of the study: age, education level, captain's license status, and method of fishing. The instrument was designed to provide data sufficient to answer the three research questions of the study. 1. What are the learning preferences of commercial fishermen? 2. Are there differences in the learning preferences of commercial fishermen across the eight geographical regions of the study? 3. Are there differences in the learning preferences of commercial fishermen based on the demographical variables? The commercial fishermen showed obvious inclinations toward specific learning preference dimensions. The fishermen indicated that they preferred the active (rather than the reflective) dimension, the sensing (rather than the intuitive) dimension, the visual (rather than the verbal) dimension, and the sequential (rather than the global) dimension. The participant's responses were similar across the eight regions. Where differences existed, they were related to the sensing/intuitive and sequential/global learning preferences dimensions. Region 8 Alaska appeared to have stronger sensing and sequential learning preferences than the other regions. Age did not appear to influence the learning preferences of the fishermen. The majority of the respondents indicated they were high school graduates. However, education did not appear to affect the learning preferences of the fishermen. Captain's license status had no influence on the learning preferences of the commercial fishermen, since the majority of the respondents did not possess a captain's license. Respondents indicated that the largest percentage of commercial fishing used net fishing methods as their primary means of fishing. For the majority of the commercial fishermen, method of fishing did not appear to influence the learning preferences of commercial fishermen. However, net and trap fishermen exhibited significant differences related to the sensing/intuitive and sequential/global learning preference dimensions and reported more preference for the sequential/global learning preference dimensions then fishermen using other methods of fishing. Implications and recommendations for further study are enumerated in the last chapter.
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Qomariyah, Nunung Nurul. "Pairwise preferences learning for recommender systems." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/20365/.

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Preference learning (PL) plays an important role in machine learning research and practice. PL works with an ordinal dataset, used frequently in areas such as behavioural science, medical science, education, psychology and social science. The aim of PL is to predict the preference for a new set of items based on the training data. In the application area of Recommender Systems (RSs), PL is used as an important element to produce good recommendations. Many ideas have been developed to build better recommendation techniques. One of the challenges in RSs is how to develop systems that are proactive and unobtrusive. To address this problem, we have studied the use of pairwise comparisons in preference elicitation as a very simple way of expressing preferences. Research in PL has also discovered this kind of representation and considers it to be learning from binary relations. There are three contributions in this thesis: The first and the most significant contribution is a new approach based on Inductive Logic Programming (ILP) in Description Logics (DL) representation to learn the relation of order. The second contribution is a strategy based on Active Learning (AL) to support the inference process and make choices more informative for learning purposes. A third contribution is a recommender system algorithm based on the ILP in DL approach, implemented in a real-world recommender system with a large used-car dataset. The proposed approach has been evaluated by using both offline and online experiments. The offline experiments were performed using two publicly available preference datasets, while the online experiment was conducted using 24 participants to evaluate the system. In the offline experiments, the overall accuracy of our proposed approach outperformed the other 3 baseline algorithms, SVM, Decision Tree and Aleph. In the online experiment, the user study also showed some satisfactory results in which our proposed pairwise comparisons interface in a recommender system beat a common standard list interface.
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Sobrie, Olivier. "Learning preferences with multiple-criteria models." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC057/document.

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L’aide multicritère à la décision (AMCD) vise à faciliter et améliorer la qualité du processus de prise de décision. Les méthodes d’AMCD permettent de traiter les problèmes de choix, rangement et classification. Ces méthodes impliquent généralement la construction d’un modèle. Déterminer les valeurs des paramètres de ces modèles n’est pas aisé. Les méthodes d’apprentissage indirectes permettent de simplifier cette tâche en apprenant les paramètres du modèle de décision à partir de jugements émis par un décideur tels que “l’alternative a est préférée à l’alternative b” ou “l’alternative a doit être classifiée dans la meilleure catégorie”. Les informations données par le décideur sont généralement parcimonieuses. Le modèle d’AMCD est appris au cours d’un processus interactif entre le décideur et l’analyste. L’analyste aide le décideur à formuler et revoir ses jugements si nécessaire. Le processus s’arrête une fois qu’un modèle satisfaisant les préférences du décideur a été trouvé. Le “preference learning” (PL) est un sous domaine du “machine learning” qui s’intéresse à l’apprentissage des préférences. Les algorithmes de ce domaine sont capables de traiter de grands jeux de données et sont validés au moyen de jeux de données artificiels et réels. Les jeux de données traités en PL sont généralement collectés de différentes sources et sont entachés de bruit.Contrairement à l’AMCD, il existe peu ou pas d’interaction avec l’utilisateur en PL. Le jeu de données fourni en entrée à l’algorithme est considéré comme un échantillon éventuellement bruité d’une “réalité” ou “vérité de terrain”. Les algorithmes utilisés dans ce domaine ont des propriétés statistiques fortes leur permettant de s’affranchir du bruit dans ces jeux de données. Dans cette thèse, nous développons des algorithmes d’apprentissage permettant d’apprendre lesparamètres de modèles d’AMCD. Plus précisément, nous développons une métaheuristique afin d’apprendre les paramètres d’un modèle appelé MR-Sort (“majority rule sorting”). Cette métaheuristique est testée sur des jeux de donnéesartificiels et réels utilisés dans le domaine du PL. Nous utilisons cet algorithme afin de traiter un problème concret dans le domaine médical. Ensuite nous modifions la métaheuristique afin d’apprendre les paramètres d’un modèle plus expressif appelé NCS (“non-compensatory sorting”). Finalement, nous développons un nouveau type de règle de veto pour les modèles MR-Sort et NCS qui permet de prendre les coalitions de critères en compte. La dernière partie de la thèse introduit les méthodes d’optimisation semi-définie positive (SDP) dans le contexte de l’aide multicritère à la décision. Précisément, nous utilisons l’optimisation SDP afin d’apprendre les paramètres d’un modèle de fonction de valeur additive
Multiple-criteria decision analysis (MCDA) aims at providing support in order to make a decision. MCDA methods allow to handle choice, ranking and sorting problems. These methods usually involve the elicitation of models. Eliciting the parameters of these models is not trivial. Indirect elicitation methods simplify this task by learning the parameters of the decision model from preference statements issued by the decision maker (DM) such as “alternative a is preferred to alternative b” or “alternative a should be classified in the best category”. The information provided by the decision maker are usually parsimonious. The MCDA model is learned through an interactive process between the DM and the decision analyst. The analyst helps the DM to modify and revise his/her statements if needed. The process ends once a model satisfying the preferences of the DM is found. Preference learning (PL) is a subfield of machine learning which focuses on the elicitation of preferences. Algorithms in this subfield are able to deal with large data sets and are validated withartificial and real data sets. Data sets used in PL are usually collected from different sources and aresubject to noise. Unlike in MCDA, there is little or no interaction with the user in PL. The input data set is considered as a noisy sample of a “ground truth”. Algorithms used in this field have strong statistical properties that allow them to filter noise in the data sets.In this thesis, we develop learning algorithms to infer the parameters of MCDA models. Precisely, we develop a metaheuristic designed for learning the parameters of a MCDA sorting model called majority rule sorting (MR-Sort) model. This metaheuristic is assessed with artificial and real data sets issued from the PL field. We use the algorithm to deal with a real application in the medical domain. Then we modify the metaheuristic to learn the parameters of a more expressive model called the non-compensatory sorting (NCS) model. After that, we develop a new type of veto rule for MR-Sort and NCS models which allows to take criteria coalitions into account. The last part of the thesis introduces semidefinite programming (SDP) in the context of multiple-criteria decision analysis. We use SDP to learn the parameters of an additive value function model
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Zhu, Ying. "PREFERENCES: OPTIMIZATION, IMPORTANCE LEARNING AND STRATEGIC BEHAVIORS." UKnowledge, 2016. http://uknowledge.uky.edu/cs_etds/46.

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Preferences are fundamental to decision making and play an important role in artificial intelligence. Our research focuses on three group of problems based on the preference formalism Answer Set Optimization (ASO): preference aggregation problems such as computing optimal (near optimal) solutions, strategic behaviors in preference representation, and learning ranks (weights) for preferences. In the first group of problems, of interest are optimal outcomes, that is, outcomes that are optimal with respect to the preorder defined by the preference rules. In this work, we consider computational problems concerning optimal outcomes. We propose, implement and study methods to compute an optimal outcome; to compute another optimal outcome once the first one is found; to compute an optimal outcome that is similar to (or, dissimilar from) a given candidate outcome; and to compute a set of optimal answer sets each significantly different from the others. For the decision version of several of these problems we establish their computational complexity. For the second topic, the strategic behaviors such as manipulation and bribery have received much attention from the social choice community. We study these concepts for preference formalisms that identify a set of optimal outcomes rather than a single winning outcome, the case common to social choice. Such preference formalisms are of interest in the context of combinatorial domains, where preference representations are only approximations to true preferences, and seeking a single optimal outcome runs a risk of missing the one which is optimal with respect to the actual preferences. In this work, we assume that preferences may be ranked (differ in importance), and we use the Pareto principle adjusted to the case of ranked preferences as the preference aggregation rule. For two important classes of preferences, representing the extreme ends of the spectrum, we provide characterizations of situations when manipulation and bribery is possible, and establish the complexity of the problem to decide that. Finally, we study the problem of learning the importance of individual preferences in preference profiles aggregated by the ranked Pareto rule or positional scoring rules. We provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decided all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples is NP-hard. We obtain similar results for the case of weighted profiles.
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Kaiser, Robert Cresswell. "Adult Learning: Evaluation of Preferences for Technology and Learning Sources for Workplace Learning." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955033/.

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The purpose of this research was to provide an initial investigation of the preferences for both technology and learning sources that are available today in the modern workplace at a large financial institution with a national presence in the USA. In addition to the preferences of the participants, the research includes insights about the culture of the learning organization by using the Dimension of Learning Organization Questionnaire (DLOQ) and two preference surveys. The research methods used in this study are categorized as mixed methods and include both quantitative and qualitative methods. This study is nonpositivist and descriptive. It is based on a triangulation design method which is comprised of analysis from data obtained from the DLOQ and preference surveys, as well as semi-structured interviews with several survey participants. The results of the studies provide the foundational information for an extended quantitative analysis.
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Foley, Nancy E. "Learning style preferences of undergraduate students with and without learning disabilities /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842527.

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Gallacher, Sarah. "Learning preferences for personalisation in a pervasive environment." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2476.

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With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users.
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Park, Kyounga. "Learning user preferences for intelligent adaptive in-vehicle navigation." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506034.

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Bergling, Oscar. "Evaluation of machine learning methods to predict payment preferences." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264504.

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The last couple of years machine learning has seen a renaissance, with Artificial Neural Networks in particular rising to prominence. The technology is being adopted by more and more businesses, with varying degrees of success. Klarna has already been experimenting with machine learning to predict payment preferences, however currently a hybrid between ad-hoc rules and a random forest model is being used in production. This report aims to find out if a pure machine learning algorithm can outperform a hybrid system for this purpose. To achieve this, four methods were tested; Random Forest, Artificial Neural Net- work, Support Vector Machine and Logistic Regression model. Three of these models outperformed the model in production. Best of these were the Artificial Neural Network which, with a cutoff threshold designed to achieve the same precision, achieved 10 percentage points higher recall. By combining the probabilities produced by an Artificial Neural Network and a Random Forest, even better results could be achieved. That method achieved 11.5 percentage points higher recall than production results with the same precision. It could be shown that the two methods had different strengths and were good at classifying different examples.
Explosionen av maskininlärning, och Artificiella Neurala Nätverk i synnerhet, har resulterat i att tekniken appliceras på allt fler användningsområden. Klarna har redan experimenterat med maskininlärning för att förutsäga betalmetoder, men för närvarande används en hybrid av regler och en Random-Forest modell. Denna rapport ämnar att utreda om en ren maskininlärningsmetod kan överträffa den nuvarande hybridmetoden. För att göra detta testades fyra olika metoder, Random Forest, Neurala Nätverk, Support Vector Machines och Logistic Regression. Det visade sig att tre av dessa presterade bättre än modellen i produktion. Bäst av alla metoder var Neurala Nätverk som var 10 procentenheter bättre än modellen i produktion i recall, med samma precision. Genom att kombinera sannolikheterna från en Random Forest samt ett Neuralt Nätverk kunde ännu bättre resultat uppnås, 11.5 procentenheter bättre i recall än modellen i produktion till samma precision.
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Books on the topic "Learning preferences"

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Individual preferences in e-learning. Aldershot, Hants, England: Gower, 2003.

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Bargar, June R. Discovering learning preferences and learning differences in the classroom. Columbus, Ohio: Ohio Agricultural Education Curriculum Materials Service, Ohio State University, 1994.

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1958-, Robson Graeme, and Smith Richard 1961-, eds. Sports coaching and learning: Using learning preferences to enhance performance. Christchurch, N.Z: N.D. Fleming, G. Robson & R. Smith, 2005.

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Handbook of intellectual styles: Preferences in cognition, learning, and thinking. New York: Springer Pub. Co., 2012.

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Differentiating by student learning preferences: Strategies and lesson plans. Larchmont, NY: Eye On Education, 2008.

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Cameron, Jane. Continuing education learning preferences and styles of legal clinic lawyers. St. Catharines, Ont: Brock University, Faculty of Education, 2006.

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Breen, Tara June Mary. An exploration of student nurses' preferences of teaching/learning strategies. (s.l: The Author), 2002.

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Anderson, Gordon J. Do preferences and-or skills explain gender based differences in learning? Toronto, Ont: University of Toronto, Department of Economics and Institute for Policy Analysis, 1994.

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Wilson, Edwin L. A study of the cognitive styles and learning preferences of fire service officers. Birmingham: University of Birmingham, 1999.

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Reaching and teaching the child with autism spectrum disorder: Using learning preferences and strengths. London: Jessica Kingsley, 2008.

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Book chapters on the topic "Learning preferences"

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Learning from Preferences." In Encyclopedia of Machine Learning, 580. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_457.

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Chevaleyre, Yann, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, and Bruno Zanuttini. "Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets." In Preference Learning, 273–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_13.

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Hüllermeier, Eyke, and Johannes Fürnkranz. "Learning from Label Preferences." In Lecture Notes in Computer Science, 38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24412-4_5.

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Hüllermeier, Eyke, and Johannes Fürnkranz. "Learning from Label Preferences." In Discovery Science, 2–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24477-3_2.

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Yang, Fang-Ying, and Yi-Chun Chen. "Learner Preferences and Achievement." In Encyclopedia of the Sciences of Learning, 1750–54. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_636.

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Hüllermeier, Eyke, and Johannes Fürnkranz. "Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants." In Preferences and Similarities, 283–304. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-85432-7_12.

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Ailon, Nir. "Learning and Optimizing with Preferences." In Lecture Notes in Computer Science, 13–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40935-6_2.

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Van Dyke Parunak, H. "Learning Actor Preferences by Evolution." In Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas, 85–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96188-6_7.

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Baum, Susan M., Robin M. Schader, and Steven V. Owen. "Multiple Intelligences and Personality Preferences." In To Be Gifted & Learning Disabled, 83–99. 3rd ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003236160-9.

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Aydoğan, Reyhan, and Pınar Yolum. "The Effect of Preference Representation on Learning Preferences in Negotiation." In New Trends in Agent-Based Complex Automated Negotiations, 3–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24696-8_1.

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Conference papers on the topic "Learning preferences"

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Seimetz, Valentin, Rebecca Eifler, and Jörg Hoffmann. "Learning Temporal Plan Preferences from Examples: An Empirical Study." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/572.

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Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as good/bad. We leverage prior work on LTL formula learning to extract a preference from these examples. We conduct an empirical study of this approach in an oversubscription planning context, using hidden target formulas to emulate the user preferences. We explore four different methods for generating example plans, and evaluate performance as a function of domain and formula size. Overall, we find that reasonable-size target formulas can often be learned effectively.
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Tondel, Inger Anne, Åsmund Ahlmann Nyre, and Karin Bernsmed. "Learning Privacy Preferences." In 2011 Sixth International Conference on Availability, Reliability and Security (ARES). IEEE, 2011. http://dx.doi.org/10.1109/ares.2011.96.

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Burnap, Alex, Yi Ren, Honglak Lee, Richard Gonzalez, and Panos Y. Papalambros. "Improving Preference Prediction Accuracy With Feature Learning." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35440.

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Motivated by continued interest within the design community to model design preferences, this paper investigates the question of predicting preferences with particular application to consumer purchase behavior: How can we obtain high prediction accuracy in a consumer preference model using market purchase data? To this end, we employ sparse coding and sparse restricted Boltzmann machines, recent methods from machine learning, to transform the original market data into a sparse and high-dimensional representation. We show that these ‘feature learning’ techniques, which are independent from the preference model itself (e.g., logit model), can complement existing efforts towards high-accuracy preference prediction. Using actual passenger car market data, we achieve significant improvement in prediction accuracy on a binary preference task by properly transforming the original consumer variables and passenger car variables to a sparse and high-dimensional representation.
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Guntly, Lisa M., and Daniel R. Tauritz. "Learning individual mating preferences." In the 13th annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001576.2001721.

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Smit, Imelda. "WhatsApp with learning preferences?" In 2015 IEEE Frontiers in Education Conference (FIE). IEEE, 2015. http://dx.doi.org/10.1109/fie.2015.7344366.

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Crochepierre, Laure, Lydia Boudjeloud-Assala, and Vincent Barbesant. "Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/849.

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In this work, we propose an interactive platform to perform grammar-guided symbolic regression using a reinforcement learning approach from human-preference feedback. To do so, a reinforcement learning algorithm iteratively generates symbolic expressions, modeled as trajectories constrained by grammatical rules, from which a user shall elicit preferences. The interface gives the user three distinct ways of stating its preferences between multiple sampled symbolic expressions: categorizing samples, comparing pairs, and suggesting improvements to a sampled symbolic expression. Learning from preferences enables users to guide the exploration in the symbolic space toward regions that are more relevant to them. We provide a web-based interface testable on symbolic regression benchmark functions and power system data.
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Pereira, Fabiola S. F., Gina M. B. Oliveira, and João Gama. "User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8129.

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The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.
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Nguyen, Trong T., and Hady W. Lauw. "Representation Learning for Homophilic Preferences." In RecSys '16: Tenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2959100.2959157.

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Zhang, Wei, and Chris Challis. "Learning User Preferences Without Feedbacks." In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2021. http://dx.doi.org/10.1109/dsaa53316.2021.9564131.

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Slama, Olfa, and Anis Yazidi. "Learning Fuzzy SPARQL User Preferences." In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00207.

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Reports on the topic "Learning preferences"

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Crawford, Elisabeth, and Manuela Veloso. Learning Dynamic Time Preferences in Multi-Agent Meeting Scheduling. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada457066.

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Hoffner, Elizabeth. A study of the perceptual learning style preferences of Japanese students. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6153.

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McFarland, Mary. An Analysis of the Relationship Between Learning Style Perceptual Preferences and Attitudes Toward Computer-Assisted Instruction. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1228.

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Bano, Masooda, and Daniel Dyonisius. Community-Responsive Education Policies and the Question of Optimality: Decentralisation and District-Level Variation in Policy Adoption and Implementation in Indonesia. Research on Improving Systems of Education (RISE), August 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/108.

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Decentralisation, or devolving authority to the third tier of government to prioritise specific policy reforms and manage their implementation, is argued to lead to pro-poor development for a number of reasons: local bureaucrats can better gauge the local needs, be responsive to community demands, and, due to physical proximity, can be more easily held accountable by community members. In the education sector, devolving authority to district government has thus been seen as critical to introducing reforms aimed at increasing access and improving learning outcomes. Based on fieldwork with district-level education bureaucracies, schools, and communities in two districts in the state of West Java in Indonesia, this article shows that decentralisation has indeed led to community-responsive policy-development in Indonesia. The district-level education bureaucracies in both districts did appear to prioritise community preferences when choosing to prioritise specific educational reforms from among many introduced by the national government. However, the optimality of these preferences could be questioned. The prioritised policies are reflective of cultural and religious values or immediate employment considerations of the communities in the two districts, rather than being explicitly focused on improving learning outcomes: the urban district prioritised degree completion, while the rural district prioritised moral education. These preferences might appear sub-optimal if the preference is for education bureaucracies to focus directly on improving literacy and numeracy outcomes. Yet, taking into account the socio-economic context of each district, it becomes easy to see the logic dictating these preferences: the communities and the district government officials are consciously prioritising those education policies for which they foresee direct payoffs. Since improving learning outcomes requires long-term commitment, it appears rational to focus on policies promising more immediate gains, especially when they aim, indirectly and implicitly, to improve actual learning outcomes. Thus, more effective community mobilisation campaigns can be developed if the donor agencies funding them recognise that it is not necessarily the lack of information but the nature of the local incentive structures that shapes communities’ expectations of education. Overall, decentralisation is leading to more context-specific educational policy prioritisation in Indonesia, resulting in the possibility of significant district-level variation in outcomes. Further, looking at the school-level variation in each district, the paper shows that public schools ranked as high performing had students from more privileged socio-economic backgrounds and were catering for communities that had more financial resources to support activities in the school, compared with schools ranked as low performing. Thus, there is a gap to bridge within public schools and not just between public and private schools.
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Shyshkina, Mariya P. Сервісні моделі формування хмаро орієнтованого середовища вищого навчального закладу. [б. в.], August 2018. http://dx.doi.org/10.31812/0564/2449.

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The article is devoted to creating and development of the cloud based educational and scientific environment of higher education institutions, using modern approaches to the ICT infrastructure design, based on the different types of service models, including public, corporate or hybrid clouds. Object of the study: to conduct the theoretical analysis of the research trends of the cloud based higher education institution ICT infrastructure modeling in the context of the tendencies of the ICT development and standardization. Object of the study: the process of formation and development of the educational and research environment in the higher education institution. The purpose of the article: to reveal the current trends of the cloud-based service models of the learning environment design and implementation. The methods of the study: The analysis of scientific and educational literature on pro-research problems; domestic and foreign experience on the emerging ICT implementation in the learning process. Results: The main types of the service models of design and deploy the cloud-based infrastructure of the educational institution are revealed; the advantages and disadvantages of the cloud-based approach are considered; the promising ways of implementation are considered. Conclusions: there are promising ways of the learning environment cloud-based service models design and application, taking into consideration its preferences and disadvantages for the certain case study.
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Munasinghe, Lalith, and Nachum Sicherman. Wage Dynamics and Unobserved Heterogeneity: Time Preference of Learning Ability? Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/w11031.

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Ma, Yoon Jin, and Kim HongYoun Hahn. Job Expectations, Job Preference, and Learning Expectations of Apparel Merchandising and Design College Students. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-766.

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Bano, Masooda, and Daniel Dyonisius. The Role of District-Level Political Elites in Education Planning in Indonesia: Evidence from Two Districts. Research on Improving Systems of Education (RISE), August 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/109.

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Focus on decentralisation as a way to improve service delivery has led to significant research on the processes of education-policy adoption and implementation at the district level. Much of this research has, however, focused on understanding the working of the district education bureaucracies and the impact of increased community participation on holding teachers to account. Despite recognition of the role of political elites in prioritising investment in education, studies examining this, especially at the district-government level, are rare. This paper explores the extent and nature of engagement of political elites in setting the education-reform agenda in two districts in the state of West Java in Indonesia: Karawang (urban district) and Purwakarta (rural district). The paper shows that for a country where the state schooling system faces a serious learning crisis, the district-level political elites do show considerable levels of engagement with education issues: governments in both districts under study allocate higher percentages of the district-government budget to education than mandated by the national legislation. However, the attitude of the political elites towards meeting challenges to the provision of good-quality education appears to be opportunistic and tokenistic: policies prioritised are those that promise immediate visibility and credit-taking, help to consolidate the authority of the bupati (the top political position in the district-government hierarchy), and align with the ruling party’s political positioning or ideology. A desire to appease growing community demand for investment in education rather than a commitment to improving learning outcomes seems to guide the process. Faced with public pressure for increased access to formal employment opportunities, the political elites in the urban district have invested in providing scholarships for secondary-school students to ensure secondary school completion, even though the district-government budget is meant for primary and junior secondary schools. The bupati in the rural district, has, on the other hand, prioritised investment in moral education; such prioritisation is in line with the community's preferences, but it is also opportunistic, as increased respect for tradition also preserves reverence for the post of the bupati—a position which was part of the traditional governance system before being absorbed into the modern democratic framework. The paper thus shows that decentralisation is enabling communities to make political elites recognise that they want the state to prioritise education, but that the response of the political elites remains piecemeal, with no evidence of a serious commitment to pursuing policies aimed at improving learning outcomes. Further, the paper shows that the political culture at the district level reproduces the problems associated with Indonesian democracy at the national level: the need for cross-party alliances to hold political office, and resulting pressure to share the spoils. Thus, based on the evidence from the two districts studied for this paper, we find that given the competitive and clientelist nature of political settlements in Indonesia, even the district level political elite do not seem pressured to prioritise policies aimed at improving learning outcomes.
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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. Learning from Nature How to Design Anti-insect Selective Pesticides - Clarification of the Interacting Face between Insecticidal Toxins and their Na-channel Receptors. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7697101.bard.

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Structural details on the interacting faces of toxins and sodium channels (Navs), and particularly identification of elements that confer specificity for insects, are difficult to approach and require suitable experimental systems. Therefore, natural toxins capable of differential recognition of insect and mammalian Navs are valuable leads for design of selective compounds in insect control. We have characterized several scorpion toxins that vary in preference for insect and mammalian Navs, and identified residues important for their action. However, despite many efforts worldwide, only little is known about the receptor sites of these toxins, and particularly on differences between these sites on insect and mammalian Navs. Another problem arises from the massive overuse of chemical insecticides, which increases resistance buildup among various insect pests. A possible solution to this problem is to combine different insecticidal compounds, especially those that provide synergic effects. Our recent finding that combinations of insecticidal receptor site-3 toxins (sea anemone and scorpion alpha) with scorpion beta toxins or their truncated derivatives are synergic in toxicity to insects is therefore timely and strongly supports this approach. Our ability to produce toxins and various Navs in recombinant forms, enable thorough analysis and structural manipulations of both toxins and receptors. On this basis we propose to (1) restrict by mutagenesis the activity of insecticidal scorpion -toxins and sea anemone toxins to insects, and clarify the molecular basis of their synergic toxicity with antiinsect selective -toxins; (2) identify Nav elements that interact with scorpion alpha and sea anemone toxins and those that determine toxin selectivity to insects; (3) determine toxin-channel pairwise side-chain interactions by thermodynamic mutant cycle analysis using our large collection of mutant -toxins and Nav mutants identified in aim 2; (4) clarify the mode of interaction of truncated -toxins with insect Navs, and elucidate how they enhance the activity of insecticidal site-3 toxins. This research may lead to rational design of novel anti-insect peptidomimetics with minimal impact on human health and the environment, and will establish the grounds for a new strategy in insect pest control, whereby a combination of allosterically interacting compounds increase insecticidal action and reduce risks of resistance buildup.
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Landau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor, and Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7587731.bard.

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Background. Brush encroachment constitutes a serious problem in both Texas and Israel. We addressed the issue of efficacy of livestock herbivory - in the form of goat browsing - to change the ecological balance to the detriment of the shrub vegetation. Shrub consumption by goats is kept low by plant chemical defenses such as tannins and terpenes. Scientists at TAES and ARO have developed an innovative, cost-effective methodology using fecal Near Infrared Spectrometry to elucidate the dietary percentage of targeted, browse species (terpene-richredberry and blueberry juniper in the US, and tannin-rich Pistacialentiscus in Israel) for a large number of animals. The original research objectives of this project were: 1. to clarify the relative preference of goat breeds and the individual variation of goats within breeds, when consuming targeted brush species; 2. to assess the heritability of browse intake and validate the concept of breeding goat lines that exhibit high preference for chemically defended brush, using juniper as a model; 3. to clarify the relative contributions of genetics and learning on the preference for target species; 4. to identify mechanisms that are associated with greater intake of brush from the two target species; 5. to establish when the target species are the most vulnerable to grazing. (Issue no.5 was addressed only partly.) Major conclusions, solutions, achievements: Both the Israel and US scientists put significant efforts into improving and validating the technique of Fecal NIRS for predicting the botanical composition of goat diets. Israeli scientists validated the use of observational data for calibrating fecal NIRS, while US scientists established that calibrations could be used across animals differing in breed and age but that caution should be used in making comparisons between different sexes. These findings are important because the ability to select goat breeds or individuals within a breed for maximal efficiency of brush control is dependent upon accurate measurement of the botanical composition of the diet. In Israel it was found that Damascus goats consume diets more than twice richer in P. lentiscus than Mamber or Boer goats. In the US no differences were found between Angora and Boer cross goats but significant differences were found between individuals within breeds in juniper dietary percentage. In both countries, intervention strategies were found that further increased the consumption of the chemically defended plant. In Israel feeding polyethylene glycol (PEG, MW 4,000) that forms high-affinity complexes with tannins increased P. lentiscus dietary percentage an average of 7 percentage units. In the US feeding a protein supplement, which enhances rates of P450-catalyzed oxidations and therefore the rate of oxidation of monoterpenes, increased juniper consumption 5 percentage units. However, the effects of these interventions were not as large as breed or individual animal effects. Also, in a wide array of competitive tannin-binding assays in Israel with trypsin, salivary proteins did not bind more tannic acid or quebracho tannin than non-specific bovine serum albumin, parotid saliva did not bind more tannins than mixed saliva, no response of tannin-binding was found to levels of dietary tannins, and the breed effect was of minor importance, if any. These fundings strongly suggest that salivary proteins are not the first line of defense from tannin astringency in goats. In the US relatively low values for heritability and repeatability for juniper consumption were found (13% and 30%, respectively), possibly resulting from sampling error or non-genetic transfer of foraging behavior, i.e., social learning. Both alternatives seem to be true as significant variation between sequential observations were noted on the same animal and cross fostering studies conducted in Israel demonstrated that kids raised by Mamber goats showed lower propensity to consume P. lentiscus than counterparts raised by Damascus goats.
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