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Academic literature on the topic 'Retrieval-based learning'

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Published: 10 December 2022
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Journal articles on the topic "Retrieval-based learning"

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Karpicke, Jeffrey D. "Retrieval-Based Learning." Current Directions in Psychological Science 21, no. 3 (May 30, 2012): 157–63. http://dx.doi.org/10.1177/0963721412443552.

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Fazio, Lisa K., and Elizabeth J. Marsh. "Retrieval-Based Learning in Children." Current Directions in Psychological Science 28, no. 2 (January 7, 2019): 111–16. http://dx.doi.org/10.1177/0963721418806673.

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Testing oneself with flash cards, using a clicker to respond to a teacher’s questions, and teaching another student are all effective ways to learn information. These learning strategies work, in part, because they require the retrieval of information from memory, a process known to enhance later memory. However, little research has directly examined retrieval-based learning in children. We review the emerging literature on the benefits of retrieval-based learning for preschool and elementary school students and draw on other literatures for further insights. We reveal clear evidence for the benefits of retrieval-based learning in children (starting in infancy). However, we know little about the developmental trajectory. Overall, the benefits are largest when the initial retrieval practice is effortful but successful.
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Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

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Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy
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Blunt, Janell R., and Jeffrey D. Karpicke. "Learning with retrieval-based concept mapping." Journal of Educational Psychology 106, no. 3 (2014): 849–58. http://dx.doi.org/10.1037/a0035934.

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Sanders, Lia Lira Olivier, Randal Pompeu Ponte, Antônio Brazil Viana Júnior, Arnaldo Aires Peixoto Junior, Marcos Kubrusly, and Antônio Miguel Furtado Leitão. "Retrieval-Based Learning in Neuroanatomy Classes." Revista Brasileira de Educação Médica 43, no. 4 (December 2019): 92–98. http://dx.doi.org/10.1590/1981-52712015v43n4rb20180184ingles.

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ABSTRACT Medical schools are continuously challenged to develop teaching modalities that improve understanding and retention of anatomical knowledge. Traditionally, learning has been regarded as the encoding of new knowledge, whereas retrieval has been considered a means for assessing learning. A solid body of research demonstrates that retrieval practice is a way to promote learning that is robust, durable, and transferable to new contexts. It involves having learners set aside the material they are learning and practice actively reconstructing it on their own. A general challenge is to develop ways to implement retrieval-based learning in educational settings. We developed a pedagogical approach that implements retrieval-based learning in practical neuroanatomy classes, which differs from usual neuroanatomy teaching in that it actively engages students through active learning. It requires students to retrieve anatomical knowledge in oral and written form, as well as to identify structures in cadaveric material. Practical anatomy classes have traditionally relied on students’ passive exposure to cadaveric material, with the lecturer pointing to and naming anatomical structures. Since August 2014, we have been applying retrieval practice in neuroanatomy classes. A total of 720 students were included in the study. Student performance one week after the practical lesson was higher in the traditional method group than in the retrieval-based learning group (p < 0.0001, effect size = 0.60). Four weeks after the intervention, however, the performance of students who learned using a retrieval-based approach was higher than that of students passively exposed to the learning material (p < 0.0001, effect size = 0.75). Taken together, our results suggest that retrieval-based learning has a greater effect on long-term retention. Retrieval-based learning is easy to apply and cost-effective. It can be implemented in nearly any educational setting. We hope that our report may inspire educators to adopt retrieval practice approaches and seek ways to apply methods from learning research in actual classrooms.
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Li, Yueli, Rongfang Bie, Chenyun Zhang, Zhenjiang Miao, Yuqi Wang, Jiajing Wang, and Hao Wu. "Optimized learning instance-based image retrieval." Multimedia Tools and Applications 76, no. 15 (September 20, 2016): 16749–66. http://dx.doi.org/10.1007/s11042-016-3950-9.

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B, Gomathi. "Semantic Web Application in E-learning Using Protege based on Information Retrieval." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1847–55. http://dx.doi.org/10.5373/jardcs/v12sp7/20202297.

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Aziz, Noor Azizah Bt. "Choosing Appropriate Retrieval based Learning Elements among Students in Java Programming Course." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 5448–55. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020251.

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Ramirez-Arellano, Aldo, Juan Bory-Reyes, and Luis Manuel Hernández-Simón. "Learning Object Retrieval and Aggregation Based on Learning Styles." Journal of Educational Computing Research 55, no. 6 (December 6, 2016): 757–88. http://dx.doi.org/10.1177/0735633116681303.

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The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.
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Karpicke, Jeffrey D., and Phillip J. Grimaldi. "Retrieval-Based Learning: A Perspective for Enhancing Meaningful Learning." Educational Psychology Review 24, no. 3 (August 4, 2012): 401–18. http://dx.doi.org/10.1007/s10648-012-9202-2.

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Dissertations / Theses on the topic "Retrieval-based learning"

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Maleki-Dizaji, Saeedeh. "Evolutionary learning multi-agent based information retrieval systems." Thesis, Sheffield Hallam University, 2003. http://shura.shu.ac.uk/6856/.

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The volume and variety of information available on the Internet has experienced exponential growth, presenting a difficulty to users wishing to obtain information that accurately matches their interests. A number of factors affect the accuracy of matching user interests and the retrieved documents. First, is the fact that users often do not present queries to information retrieval systems in the form that optimally represents the information they want. Secondly, the measure of a document's relevance is highly subjective and variable between different users. This thesis addresses this problem with an adaptive approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. The method combines a qualitative feedback measure obtained using fuzzy inference, and quantitative feedback based on evolutionary algorithms (Genetic Algorithms) fitness measures. Furthermore, the retrieval system's design approach is based on a multi-agent design approach, in order to handle the complexities of the information retrieval system including: document indexing, relevance feedback, user modelling, filtering and ranking the retrieve documents. The major contribution of this research are the combination of genetic algorithms and fuzzy relevance feedback for modelling adaptive behaviour, which is compared against conventional relevance feedback. Novel Genetic Algorithms operators are proposed within the context of textual; the encoding and vector space model for document representation is generalised within the same context.
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Wu, Mengjiao. "Retrieval-based Metacognitive Monitoring in Self-regulated Learning." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532049448140424.

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Chafik, Sanaa. "Machine learning techniques for content-based information retrieval." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL008/document.

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Avec l’évolution des technologies numériques et la prolifération d'internet, la quantité d’information numérique a considérablement évolué. La recherche par similarité (ou recherche des plus proches voisins) est une problématique que plusieurs communautés de recherche ont tenté de résoudre. Les systèmes de recherche par le contenu de l’information constituent l’une des solutions prometteuses à ce problème. Ces systèmes sont composés essentiellement de trois unités fondamentales, une unité de représentation des données pour l’extraction des primitives, une unité d’indexation multidimensionnelle pour la structuration de l’espace des primitives, et une unité de recherche des plus proches voisins pour la recherche des informations similaires. L’information (image, texte, audio, vidéo) peut être représentée par un vecteur multidimensionnel décrivant le contenu global des données d’entrée. La deuxième unité consiste à structurer l’espace des primitives dans une structure d’index, où la troisième unité -la recherche par similarité- est effective.Dans nos travaux de recherche, nous proposons trois systèmes de recherche par le contenu de plus proches voisins. Les trois approches sont non supervisées, et donc adaptées aux données étiquetées et non étiquetées. Elles sont basées sur le concept du hachage pour une recherche efficace multidimensionnelle des plus proches voisins. Contrairement aux approches de hachage existantes, qui sont binaires, les approches proposées fournissent des structures d’index avec un hachage réel. Bien que les approches de hachage binaires fournissent un bon compromis qualité-temps de calcul, leurs performances en termes de qualité (précision) se dégradent en raison de la perte d’information lors du processus de binarisation. À l'opposé, les approches de hachage réel fournissent une bonne qualité de recherche avec une meilleure approximation de l’espace d’origine, mais induisent en général un surcoût en temps de calcul.Ce dernier problème est abordé dans la troisième contribution. Les approches proposées sont classifiées en deux catégories, superficielle et profonde. Dans la première catégorie, on propose deux techniques de hachage superficiel, intitulées Symmetries of the Cube Locality sensitive hashing (SC-LSH) et Cluster-Based Data Oriented Hashing (CDOH), fondées respectivement sur le hachage aléatoire et l’apprentissage statistique superficiel. SCLSH propose une solution au problème de l’espace mémoire rencontré par la plupart des approches de hachage aléatoire, en considérant un hachage semi-aléatoire réduisant partiellement l’effet aléatoire, et donc l’espace mémoire, de ces dernières, tout en préservant leur efficacité pour la structuration des espaces hétérogènes. La seconde technique, CDOH, propose d’éliminer l’effet aléatoire en combinant des techniques d’apprentissage non-supervisé avec le concept de hachage. CDOH fournit de meilleures performances en temps de calcul, en espace mémoire et en qualité de recherche.La troisième contribution est une approche de hachage basée sur les réseaux de neurones profonds appelée "Unsupervised Deep Neuron-per-Neuron Hashing" (UDN2H). UDN2H propose une indexation individuelle de la sortie de chaque neurone de la couche centrale d’un modèle non supervisé. Ce dernier est un auto-encodeur profond capturant une structure individuelle de haut niveau de chaque neurone de sortie.Nos trois approches, SC-LSH, CDOH et UDN2H, ont été proposées séquentiellement durant cette thèse, avec un niveau croissant, en termes de la complexité des modèles développés, et en termes de la qualité de recherche obtenue sur de grandes bases de données d'information
The amount of media data is growing at high speed with the fast growth of Internet and media resources. Performing an efficient similarity (nearest neighbor) search in such a large collection of data is a very challenging problem that the scientific community has been attempting to tackle. One of the most promising solutions to this fundamental problem is Content-Based Media Retrieval (CBMR) systems. The latter are search systems that perform the retrieval task in large media databases based on the content of the data. CBMR systems consist essentially of three major units, a Data Representation unit for feature representation learning, a Multidimensional Indexing unit for structuring the resulting feature space, and a Nearest Neighbor Search unit to perform efficient search. Media data (i.e. image, text, audio, video, etc.) can be represented by meaningful numeric information (i.e. multidimensional vector), called Feature Description, describing the overall content of the input data. The task of the second unit is to structure the resulting feature descriptor space into an index structure, where the third unit, effective nearest neighbor search, is performed.In this work, we address the problem of nearest neighbor search by proposing three Content-Based Media Retrieval approaches. Our three approaches are unsupervised, and thus can adapt to both labeled and unlabeled real-world datasets. They are based on a hashing indexing scheme to perform effective high dimensional nearest neighbor search. Unlike most recent existing hashing approaches, which favor indexing in Hamming space, our proposed methods provide index structures adapted to a real-space mapping. Although Hamming-based hashing methods achieve good accuracy-speed tradeoff, their accuracy drops owing to information loss during the binarization process. By contrast, real-space hashing approaches provide a more accurate approximation in the mapped real-space as they avoid the hard binary approximations.Our proposed approaches can be classified into shallow and deep approaches. In the former category, we propose two shallow hashing-based approaches namely, "Symmetries of the Cube Locality Sensitive Hashing" (SC-LSH) and "Cluster-based Data Oriented Hashing" (CDOH), based respectively on randomized-hashing and shallow learning-to-hash schemes. The SC-LSH method provides a solution to the space storage problem faced by most randomized-based hashing approaches. It consists of a semi-random scheme reducing partially the randomness effect of randomized hashing approaches, and thus the memory storage problem, while maintaining their efficiency in structuring heterogeneous spaces. The CDOH approach proposes to eliminate the randomness effect by combining machine learning techniques with the hashing concept. The CDOH outperforms the randomized hashing approaches in terms of computation time, memory space and search accuracy.The third approach is a deep learning-based hashing scheme, named "Unsupervised Deep Neuron-per-Neuron Hashing" (UDN2H). The UDN2H approach proposes to index individually the output of each neuron of the top layer of a deep unsupervised model, namely a Deep Autoencoder, with the aim of capturing the high level individual structure of each neuron output.Our three approaches, SC-LSH, CDOH and UDN2H, were proposed sequentially as the thesis was progressing, with an increasing level of complexity in terms of the developed models, and in terms of the effectiveness and the performances obtained on large real-world datasets
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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The image retrieval is achieved by transforming images to feature representations in a latent space, where it is possible to reason about image similarity in terms of image content. In this thesis, a self-supervised method is developed to learn feature representations of road scenes images. The self-supervised method learns feature representations for images by adapting intermediate convolutional features from an existing deep Convolutional Neural Network (CNN). A contrastive approach based on Noise Contrastive Estimation (NCE) is used to train the feature learning model. For complex images like road scenes where mutiple image aspects can occur simultaneously, it is important to embed all the salient image aspects in the feature representation. To achieve this, the output feature representation is obtained as an ensemble of feature embeddings which are learned by focusing on different image aspects. An attention mechanism is incorporated to encourage each ensemble member to focus on different image aspects. For comparison, a self-supervised model without attention is considered and a simple dimensionality reduction approach using SVD is treated as the baseline. The methods are evaluated on nine different evaluation datasets using CBIR performance metrics. The datasets correspond to different image aspects and concern the images at different spatial levels - global, semi-global and local. The feature representations learned by self-supervised methods are shown to perform better than the SVD approach. Taking into account that no labelled data is required for training, learning representations for road scenes images using self-supervised methods appear to be a promising direction. Usage of multiple query images to emphasize a query intention is investigated and a clear improvement in CBIR performance is observed. It is inconclusive whether the addition of an attentive mechanism impacts CBIR performance. The attention method shows some positive signs based on qualitative analysis and also performs better than other methods for one of the evaluation datasets containing a local aspect. This method for learning feature representations is promising but requires further research involving more diverse and complex image aspects.
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Alzu’bi, Ahmad Gazi Suleiman. "Semantic content-based image retrieval using compact multifeatures and deep learning." Thesis, University of the West of Scotland, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738480.

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com, chungkp@yahoo, and Kien Ping Chung. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070831.123947.

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Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information storage. While this presents a wealth of information, it also creates a great problem in retrieving appropriate and relevant information during searching. This has resulted in an enormous growth of interest, and much active research, into the extraction of relevant information from non text-based databases. In particular,content-based image retrieval (CBIR) systems have been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analysing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminant analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labelled samples. The idea is that a smaller image sample group is used to analyse the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved. From experimental results, it is found that the retrieval accuracy for small sample data has also improved. Intelligent E-Business has been used as a case study in this thesis to demonstrate the potential of the framework in the application of image retrieval system. In addition, an inter-query framework has been proposed in this thesis. This framework is also based on the statistical discriminant analysis technique. A common approach in inter-query for a CBIR system is to apply the term-document approach. This is done by treating each image’s name or address as a term, and the query session as a document. However, scalability becomes an issue with this technique as the number of stored queries increases. Moreover, this approach is not appropriate for a dynamic image database environment. In this thesis, the proposed inter-query framework uses a cluster approach to capture the visual properties common to the previously stored queries. Thus, it is not necessary to “memorise” the name or address of the images. In order to manage the size of the user’s profile, the proposed framework also introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has outperformed the short term learning approach. It also has the advantage that it eliminates the burden of the complex database maintenance strategies required in the term-document approach commonly needed by the interquery learning framework. Lastly, the proposed inter-query learning framework has been further extended by the incorporation of a new semantic structure. The semantic structure is used to connect the previous queries both visually and semantically. This structure provides the system with the ability to retrieve images that are semantically similar and yet visually different. To do this, an active learning strategy has been incorporated for exploring the structure. Experiments have again shown that the proposed new framework has outperformed the previous framework.
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Chung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Thesis, Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/666/.

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Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information storage. While this presents a wealth of information, it also creates a great problem in retrieving appropriate and relevant information during searching. This has resulted in an enormous growth of interest, and much active research, into the extraction of relevant information from non text-based databases. In particular,content-based image retrieval (CBIR) systems have been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analysing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminant analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labelled samples. The idea is that a smaller image sample group is used to analyse the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved. From experimental results, it is found that the retrieval accuracy for small sample data has also improved. Intelligent E-Business has been used as a case study in this thesis to demonstrate the potential of the framework in the application of image retrieval system. In addition, an inter-query framework has been proposed in this thesis. This framework is also based on the statistical discriminant analysis technique. A common approach in inter-query for a CBIR system is to apply the term-document approach. This is done by treating each image's name or address as a term, and the query session as a document. However, scalability becomes an issue with this technique as the number of stored queries increases. Moreover, this approach is not appropriate for a dynamic image database environment. In this thesis, the proposed inter-query framework uses a cluster approach to capture the visual properties common to the previously stored queries. Thus, it is not necessary to 'memorise' the name or address of the images. In order to manage the size of the user's profile, the proposed framework also introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has outperformed the short term learning approach. It also has the advantage that it eliminates the burden of the complex database maintenance strategies required in the term-document approach commonly needed by the interquery learning framework. Lastly, the proposed inter-query learning framework has been further extended by the incorporation of a new semantic structure. The semantic structure is used to connect the previous queries both visually and semantically. This structure provides the system with the ability to retrieve images that are semantically similar and yet visually different. To do this, an active learning strategy has been incorporated for exploring the structure. Experiments have again shown that the proposed new framework has outperformed the previous framework.
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Chung, Kien Ping. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Chung, Kien- Ping (2007) Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles. PhD thesis, Murdoch University, 2007. http://researchrepository.murdoch.edu.au/666/.

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Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information storage. While this presents a wealth of information, it also creates a great problem in retrieving appropriate and relevant information during searching. This has resulted in an enormous growth of interest, and much active research, into the extraction of relevant information from non text-based databases. In particular,content-based image retrieval (CBIR) systems have been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analysing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminant analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labelled samples. The idea is that a smaller image sample group is used to analyse the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved. From experimental results, it is found that the retrieval accuracy for small sample data has also improved. Intelligent E-Business has been used as a case study in this thesis to demonstrate the potential of the framework in the application of image retrieval system. In addition, an inter-query framework has been proposed in this thesis. This framework is also based on the statistical discriminant analysis technique. A common approach in inter-query for a CBIR system is to apply the term-document approach. This is done by treating each image's name or address as a term, and the query session as a document. However, scalability becomes an issue with this technique as the number of stored queries increases. Moreover, this approach is not appropriate for a dynamic image database environment. In this thesis, the proposed inter-query framework uses a cluster approach to capture the visual properties common to the previously stored queries. Thus, it is not necessary to 'memorise' the name or address of the images. In order to manage the size of the user's profile, the proposed framework also introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has outperformed the short term learning approach. It also has the advantage that it eliminates the burden of the complex database maintenance strategies required in the term-document approach commonly needed by the interquery learning framework. Lastly, the proposed inter-query learning framework has been further extended by the incorporation of a new semantic structure. The semantic structure is used to connect the previous queries both visually and semantically. This structure provides the system with the ability to retrieve images that are semantically similar and yet visually different. To do this, an active learning strategy has been incorporated for exploring the structure. Experiments have again shown that the proposed new framework has outperformed the previous framework.
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Wu, Zutao. "Kmer-based sequence representations for fast retrieval and comparison." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103083/1/Zutao_Wu_Thesis.pdf.

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This thesis presents a study of alignment-free methods for genetic sequence comparison. By using representations based on k-mers – short subsequences of length k - sequence similarity can be measured rapidly and accurately by calculating the distance between these paired representations. This research utilises and adapts conventional methods from information retrieval to generate novel representations for k-mers and sequence fragments. Precision was further improved through the use of machine learning approaches - especially neural networks - to learn relationships between k-mers and to generate enhanced sequence representations. These approaches have applications in large scale sequence comparison, especially in the analysis of metagenomic samples.
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Shevchuk, Danylo. "Audio Moment Retrieval based on Natural Language Query." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20094.

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Background. Users spend a lot of time searching through media content to find the desirable fragment. Most of the time people can describe verbally what they are looking for but there is not much of a use for that as of today. Using that verbal description as a query to search for the right interval in a given audio sample would save people a lot of time. Objectives. The aim of this thesis is to compare the performance of the methods suitable for retrieving desired intervals from an audio of an arbitrary length using a natural language query input. There are two objectives. The first one is to train models that match a natural language input to the specific interval of a given soundtrack. The second one is to evaluate the models' performance using conventional metrics. Methods. The research method used in this research is mixed. Various literature on the existing methods suitable for audio classification was reviewed. Three models were selected for conducting the experiments. The selected models are YamNet, AlexNet and ResNet-50. Two experiments were conducted. The goal of the first experiment was to measure the models' performance on classifying audio samples. The goal of the second experiment was to measure the same models' performance on the audio intervals retrieval problem which uses classification as a part of the approach. The steps taken to conduct the experiments were reported as well as the statistical data obtained as a result of the experiments. These steps include data collection, data preprocessing, models training and their performance evaluation. Results. The two tests were conducted to see which model performs better on two separate problems - audio classification and intervals retrieval based on a natural language query. The statistical data was obtained as a result of the tests. The degree (performance-wise) to which can we match a natural language query input to a corresponding interval of an audio of an arbitrary length was calculated for each of the selected models. The aggregated performance of the models are mostly comparable, with YamNet occasionally outperforming the other two models. The average Area Under the Curve, and Accuracy for the studied models are as follows: (67, 71.62), (68.99, 67.72) and (66.59, 71.93) for YamNet, AlexNet and ResNet-50, respectively. Conclusions. We have discovered that the tested models were not capable of retrieving intervals from an audio of an arbitrary length based on a natural language query, however the degree to which the models are able to retrieve the intervals varies depending on the queried keyword and other hyperparameters such as the value of the threshold that is used to filter the audio patches that yield too low probability of the queried class.
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Books on the topic "Retrieval-based learning"

1

Azuaje, Francisco Javier. An unsupervised neural learning approach to retrieval strategies for case-based reasoning and decision support. [s.l: The Author], 1999.

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Qing, Li, Klamma Ralf, Leung Howard, Specht Marcus, and SpringerLink (Online service), eds. Advances in Web-Based Learning - ICWL 2012: 11th International Conference, Sinaia, Romania, September 2-4, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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1947-, Sharma S. K., ed. Creating knowledge based organizations. Hershey, PA: Idea Group Publishing, 2004.

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David, Hutchison. Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Advances in multimedia and network information system technologies. Berlin: Springer, 2010.

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The evolution of inquiry: Controlled, guided, modeled, and free. Santa Barbara, California: Libraries Unlimited, an imprint of ABC-CLIO, LLC, 2015.

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Jackie, Carrigan, ed. Resource-based learning activities: Information literacy for high school students. Chicago, Ill: American Library Association, 1994.

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Information entrepreneurship: Information services based on the information lifecycle. Lanham, Md: Scarecrow Press, 2005.

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Wohlgenannt, Gerhard. Learning ontology relations by combining corpus-based techniques and reasoning on data from semantic web sources. Frankfurt am Main: P. Lang, 2011.

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Wohlgenannt, Gerhard. Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources. Bern: Peter Lang International Academic Publishers, 2018.

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Book chapters on the topic "Retrieval-based learning"

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Jing, Feng, Mingjing Li, Lei Zhang, Hong-Jiang Zhang, and Bo Zhang. "Learning in Region-Based Image Retrieval." In Lecture Notes in Computer Science, 206–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_21.

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Bajwa, Manpreet Singh, Ravi Rana, and Geetanshi Bagga. "Machine Learning-Based Information Retrieval System." In Lecture Notes in Electrical Engineering, 13–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8297-4_2.

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Guo, Hui, Jie He, Caixu Xu, and Dongling Li. "Image Retrieval Algorithm Based on Fractal Coding." In Machine Learning and Intelligent Communications, 254–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04409-0_24.

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Zhou, Zhi-Hua, Ke-Jia Chen, and Yuan Jiang. "Exploiting Unlabeled Data in Content-Based Image Retrieval." In Machine Learning: ECML 2004, 525–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_48.

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Wu, Qihui, Rui Liu, Dongsheng Zhou, and Qiang Zhang. "3D Human Motion Retrieval Based on Graph Model." In E-Learning and Games, 219–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23712-7_29.

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Thornley, Clare. "Teaching Information Retrieval Through Problem-Based Learning." In Teaching and Learning in Information Retrieval, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22511-6_13.

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Świeboda, Wojciech, Michał Meina, and Hung Son Nguyen. "Weight Learning in TRSM-based Information Retrieval." In Studies in Computational Intelligence, 61–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04714-0_5.

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Ferguson, Valerie, Sheila Padden, Sigrid Rutishauser, and Michael Hollingsworth. "Information Retrieval Skills for Problem Based Learning." In Health Information Management: What Strategies?, 109–12. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8786-0_34.

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Peng, Min, Jiajia Huang, Jiahui Zhu, Li Zhou, Hui Fu, Yanxiang He, and Fei Li. "Co-Learning Ranking for Query-Based Retrieval." In Lecture Notes in Computer Science, 468–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41230-1_39.

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Huiskes, Mark J. "Aspect-Based Relevance Learning for Image Retrieval." In Lecture Notes in Computer Science, 639–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526346_67.

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Conference papers on the topic "Retrieval-based learning"

1

Zhang, Zhen-Hua, Yi-Nan Lu, Wen-Hui Li, and Gang Wang. "Segmentation-Based Image Retrieval." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370428.

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Xin Zhang, Bing Wang, Zhi-De Zhang, and Xiao-Yan Zhao. "SSVR-based image semantic retrieval." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620848.

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Biao Niu, Yifan Zhang, Jinqiao Wang, Jian Cheng, and Hanqing Lu. "Subspace learning based active learning for image retrieval." In 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2013. http://dx.doi.org/10.1109/icmew.2013.6618268.

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Wu, Chi-jiunn, Hui-chi Zeng, Szu-hao Huang, Shang-hong Lai, and Wen-hao Wang. "Learning-Based Interactive Video Retrieval System." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262898.

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Zhang, Zhen-hua, Yong Quan, Wen-hui Li, and Wu Guo. "A New Content-Based Image Retrieval." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258801.

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Ozyer, Gulsah Tumuklu, and Fatos Yarman Vural. "An Attention-Based Image Retrieval System." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.27.

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Gilbert, Adam D., Ran Chang, and Xiaojun Qi. "A retrieval pattern-based inter-query learning approach for content-based image retrieval." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5654156.

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Yu, Jerry. "Session details: Subspace learning in content-based image retrieval." In CIVR08: CIVR'08 - International Conference on Content-based Image and Video Retrieval. New York, NY, USA: ACM, 2008. http://dx.doi.org/10.1145/3247069.

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Chang, Chun-guang, Ding-wei Wang, Ya-chen Liu, and Bao-ku Qi. "Fuzzy Similarity Measure Based Case Retrieval Method." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258338.

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Tanida, Jun, and Ryoichi Horisaki. "Learning-based signal retrieval from scattering media." In SPECKLE 2018: VII International Conference on Speckle Metrology, edited by Michal Józwik, Leszek R. Jaroszewicz, and Malgorzata Kujawińska. SPIE, 2018. http://dx.doi.org/10.1117/12.2322800.

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Reports on the topic "Retrieval-based learning"

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Lee, Jung-Eun, Rong Jin, and Anil K. Jain. Ranked-Based Distance Metric Learning: An Application to Image Retrieval. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada500953.

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408937.

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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for number restrictions or for existential restrictions. Many applications, however, require to combine these constructors. In this work, we present an lcs algorithm for the description logic ALEN, which allows for both constructors (as well as concept conjunction, primitive negation, and value restrictions). The proof of correctness of our lcs algorithm is based on an appropriate structural characterization of subsumption in ALEN also introduced in this paper.
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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for number restrictions or for existential restrictions. Many applications, however, require to combine these constructors. In this work, we present an lcs algorithm for the description logic ALEN, which allows for both constructors (as well as concept conjunction, primitive negation, and value restrictions). The proof of correctness of our lcs algorithm is based on an appropriate structural characterization of subsumption in ALEN also introduced in this paper.
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