Relevant bibliographies by topics / Adaptive machine learning

Academic literature on the topic 'Adaptive machine learning'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Adaptive machine learning"

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Wilson, Craig, Yuheng Bu, and Venugopal V. Veeravalli. "Adaptive sequential machine learning." Sequential Analysis 38, no. 4 (October 2, 2019): 545–68. http://dx.doi.org/10.1080/07474946.2019.1686889.

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Wang, Gai-Ge, Mei Lu, Yong-Quan Dong, and Xiang-Jun Zhao. "Self-adaptive extreme learning machine." Neural Computing and Applications 27, no. 2 (March 21, 2015): 291–303. http://dx.doi.org/10.1007/s00521-015-1874-3.

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.
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Mundhe, Shivani. "Image Segmentation using Adaptive Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 948–50. http://dx.doi.org/10.22214/ijraset.2021.34383.

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Hie, Brian L., and Kevin K. Yang. "Adaptive machine learning for protein engineering." Current Opinion in Structural Biology 72 (February 2022): 145–52. http://dx.doi.org/10.1016/j.sbi.2021.11.002.

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Majumdar, Somshubra, Ishaan Jain, and Kunal Kukreja. "AdaSort: Adaptive Sorting using Machine Learning." International Journal of Computer Applications 145, no. 12 (July 15, 2016): 12–17. http://dx.doi.org/10.5120/ijca2016910726.

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Stange, Renata Luiza, and Joao Jose. "Applying Adaptive Technology in Machine Learning." IEEE Latin America Transactions 12, no. 7 (October 2014): 1298–306. http://dx.doi.org/10.1109/tla.2014.6948866.

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Cao, Jiuwen, Zhiping Lin, and Guang-Bin Huang. "Self-Adaptive Evolutionary Extreme Learning Machine." Neural Processing Letters 36, no. 3 (July 18, 2012): 285–305. http://dx.doi.org/10.1007/s11063-012-9236-y.

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Hasan, Sajib. "Adaptive Fitts for Adaptive Interface." AIUB Journal of Science and Engineering (AJSE) 17, no. 2 (July 31, 2018): 51–58. http://dx.doi.org/10.53799/ajse.v17i2.9.

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Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.
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Chabaud, Ulysse, Damian Markham, and Adel Sohbi. "Quantum machine learning with adaptive linear optics." Quantum 5 (July 5, 2021): 496. http://dx.doi.org/10.22331/q-2021-07-05-496.

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We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine – either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.
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Dissertations / Theses on the topic "Adaptive machine learning"

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Jelfs, Beth. "Collaborative adaptive filtering for machine learning." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/5598.

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Quantitative performance criteria for the analysis of machine learning architectures and algorithms have long been established. However, qualitative performance criteria, which identify fundamental signal properties and ensure any processing preserves the desired properties, are still emerging. In many cases, whilst offline statistical tests exist such as assessment of nonlinearity or stochasticity, online tests which not only characterise but also track changes in the nature of the signal are lacking. To that end, by employing recent developments in signal characterisation, criteria are derived for the assessment of the changes in the nature of the processed signal. Through the fusion of the outputs of adaptive filters a single collaborative hybrid filter is produced. By tracking the dynamics of the mixing parameter of this filter, rather than the actual filter performance, a clear indication as to the current nature of the signal is given. Implementations of the proposed method show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. This is then extended (in the real domain) from dealing with nonlinearity in general, to a more specific example, namely sparsity. Extensions of adaptive filters from the real to the complex domain are non-trivial and the differences between the statistics in the real and complex domains need to be taken into account. In terms of signal characteristics, nonlinearity can be both split- and fully-complex and complex-valued data can be considered circular or noncircular. Furthermore, by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion. To produce online tests for sparsity, adaptive filters for sparse environments are investigated and a unifying framework for the derivation of proportionate normalised least mean square (PNLMS) algorithms is presented. This is then extended to derive variants with an adaptive step-size. In order to create an online test for noncircularity, a study of widely linear autoregressive modelling is presented, from which a proof of the convergence of the test for noncircularity can be given. Applications of this method are illustrated on examples such as biomedical signals, speech and wind data.
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Miles, Jonathan David. "Machine Learning for Adaptive Computer Game Opponents." The University of Waikato, 2009. http://hdl.handle.net/10289/2779.

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This thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can be integrated into a modern computer game without have a detrimental effect on game performance, then experimenting with different machine learning techniques to maximize the computer player's performance. Experiments use three machine learning techniques; static prediction models, continuous learning, and reinforcement learning. Static models show the highest initial performance but are not able to beat a simple opponent. Continuous learning is able to improve the performance achieved with static models but the rate of improvement drops over time and the computer player is still unable to beat the opponent. Reinforcement learning methods have the highest rate of improvement but the lowest initial performance. This limits the effectiveness of reinforcement learning because a large number of episodes are required before performance becomes sufficient to match the opponent.
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Long, Shun. "Adaptive Java optimisation using machine learning techniques." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/567.

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There is a continuing demand for higher performance, particularly in the area of scientific and engineering computation. In order to achieve high performance in the context of frequent hardware upgrading, software must be adaptable for portable performance. What is required is an optimising compiler that evolves and adapts itself to environmental change without sacrificing performance. Java has emerged as a dominant programming language widely used in a variety of application areas. However, its architectural independant design means that it is frequently unable to deliver high performance especially when compared to other imperative languages such as Fortran and C/C++. This thesis presents a language- and architecture-independant approach to achieve portable high performance. It uses the mapping notation introduced in the Unified Transformation Framework to specify a large optimisation space. A heuristic random search algorithm is introduced to explore this space in a feedback-directed iterative optimisation manner. It is then extended using a machine learning approach which enables the compiler to learn from its previous optimisations and apply the knowledge when necessary. Both the heuristic random search algorithm and the learning optimisation approach are implemented in a prototype Adaptive Optimisation Framework for Java (AOF-Java). The experimental results show that the heuristic random search algorithm can find, within a relatively small number of atttempts, good points in the large optimisation space. In addition, the learning optimisation approach is capable of finding good transformations for a given program from its prior experience with other programs.
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Ar, Rosyid Harits. "Adaptive serious educational games using machine learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-serious-educational-games-using-machine-learning(b5f5024b-c7fd-4660-997c-9fd22e140a8f).html.

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The ultimate goals of adaptive serious educational games (adaptive SEG) are to promote effective learning and maximising enjoyment for players. Firstly, we develop the SEG by combining knowledge space (learning materials) and game content space to be used to convey learning materials. We propose a novel approach that serves toward minimising experts' involvement in mapping learning materials to game content space. We categorise both content spaces using known procedures and apply BIRCH clustering algorithm to categorise the similarity of the game content. Then, we map both content spaces based on the statistical properties and/or by the knowledge learning handout. Secondly, we construct a predictive model by learning data sets constructed through a survey on public testers who labelled their in-game data with their reported experiences. A Random Forest algorithm non-intrusively predicts experiences via the game data. Lastly, it is not feasible to manually select or adapt the content from both spaces because of the immense amount of options available. Therefore, we apply reinforcement learning technique to generate a series of learning goals that promote an efficient learning for the player. Subsequently, a combination of conditional branching and agglomerative hierarchical clustering select the most appropriate game content for each selected education material. For a proof-of-concept, we apply the proposed approach to producing the SEG, named Chem Dungeon, as a case study to demonstrate the effectiveness of our proposed methods.
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Dal, Pozzolo Andrea. "Adaptive Machine Learning for Credit Card Fraud Detection." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/221654.

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Billions of dollars of loss are caused every year by fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to the non-stationary distribution of the data, the highly unbalanced classes distributions and the availability of few transactions labeled by fraud investigators. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. In this thesis we aim to provide some answers by focusing on crucial issues such as: i) why and how undersampling is useful in the presence of class imbalance (i.e. frauds are a small percentage of the transactions), ii) how to deal with unbalanced and evolving data streams (non-stationarity due to fraud evolution and change of spending behavior), iii) how to assess performances in a way which is relevant for detection and iv) how to use feedbacks provided by investigators on the fraud alerts generated. Finally, we design and assess a prototype of a Fraud Detection System able to meet real-world working conditions and that is able to integrate investigators’ feedback to generate accurate alerts.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.

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Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des tablettes et des smartphones. Il semble naturel d'utiliser ces technologies dans un but éducatif. Cela amène de nombreuses problématiques, telles que comment faire des interfaces accessibles à tous, comment rendre des contenus pédagogiques motivants ou encore comment personnaliser les activités afin d'adapter le contenu à chacun. Au cours de cette thèse, nous avons développé des méthodes, regroupées dans un framework nommé HMABITS, afin d'adapter des séquences d'activités pédagogiques en fonction des performances et des préférences des apprenants, dans le but de maximiser leur vitesse d'apprentissage et leur motivation. Ces méthodes utilisent des modèles computationnels de motivation intrinsèque pour identifier les activités offrant les plus grands progrès d'apprentissage, et utilisent des algorithmes de Bandits Multi-Bras pour gérer le compromis exploration/exploitation à l'intérieur de l'espace d'activité. Les activités présentant un intérêt optimal sont ainsi privilégiées afin de maintenir l'apprenant dans un état de Flow ou dans sa Zone de Développement Proximal. De plus, certaines de nos méthodes permettent à l'apprenant de faire des choix sur des caractéristiques contextuelles ou le contenu pédagogique de l'application, ce qui est un vecteur d'autodétermination et de motivation. Afin d'évaluer l'efficacité et la pertinence de nos algorithmes, nous avons mené plusieurs types d'expérimentation. Nos méthodes ont d'abord été testées en simulation afin d'évaluer leur fonctionnement avant de les utiliser dans d'actuelles applications d'apprentissage. Pour ce faire, nous avons développé différents modèles d'apprenants, afin de pouvoir éprouver nos méthodes selon différentes approches, un modèle d'apprenant virtuel ne reflétant jamais le comportement d'un apprenant réel. Les résultats des simulations montrent que le framework HMABITS permet d'obtenir des résultats d'apprentissage comparables et, dans certains cas, meilleurs qu'une solution optimale ou qu'une séquence experte. Nous avons ensuite développé notre propre scénario pédagogique et notre propre serious game afin de tester nos algorithmes en situation réelle avec de vrais élèves. Nous avons donc développé un jeu sur la thématique de la décomposition des nombres, au travers de la manipulation de la monnaie, pour les enfants de 6 à 8 ans. Nous avons ensuite travaillé avec le rectorat et différentes écoles de l'académie de bordeaux. Sur l'ensemble des expérimentations, environ 1000 élèves ont travaillé sur l'application sur tablette. Les résultats des études en situation réelle montrent que le framework HMABITS permet aux élèves d'accéder à des activités plus diverses et plus difficiles, d'avoir un meilleure apprentissage et d'être plus motivés qu'avec une séquence experte. Les résultats montrent même que ces effets sont encore plus marqués lorsque les élèves ont la possibilité de faire des choix
Can computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
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Wiens, Jenna Marleau. "Machine learning for patient-adaptive ectopic beat classification." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60823.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 83-85).
Physicians require automated techniques to accurately analyze the vast amount of physiological data collected by continuous monitoring devices. In this thesis, we consider one analysis task in particular, the classification of heartbeats from electrocardiographic recordings (ECG). This problem is made challenging by the inter-patient differences present in ECG morphology and timing characteristics. Supervised classifiers trained on a collection of patients can have unpredictable results when applied to a new patient. To reduce the effect of inter-patient differences, researchers have suggested training patient-adative classifiers by training on labeled data from the test patient. However, patient-adaptive classifiers have not been integrated in practice because they require an impractical amount of patient-specific expert knowledge. We present two approaches based on machine learning for building accurate patientadaptive beat classifiers that use little or no patient-specific expert knowledge. First, we present a method to transfer and adapt knowledge from a collection of patients to a test-patient. This first approach, based on transductive transfer learning, requires no patient-specific labeled data, only labeled data from other patients. Second, we consider the scenario where patient-specific expert knowledge is available, but comes at a high cost. We present a novel algorithm for SVM active learning. By intelligently selecting the training set we show how one can build highly accurate patient-adaptive classifiers using only a small number of cardiologist supplied labels. Our results show the gains in performance possible when using patient-adaptive classifiers in place of global classifiers. Furthermore, the effectiveness of our techniques, which use little or no patient-specific expert knowledge, suggest that it is also practical to use patient-adaptive techniques in a clinical setting.
by Jenna Marleau Wiens.
S.M.
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Drolia, Utsav. "Adaptive Distributed Caching for Scalable Machine Learning Services." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1004.

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Applications for Internet-enabled devices use machine learning to process captured data to make intelligent decisions or provide information to users. Typically, the computation to process the data is executed in cloud-based backends. The devices are used for sensing data, offloading it to the cloud, receiving responses and acting upon them. However, this approach leads to high end-to-end latency due to communication over the Internet. This dissertation proposes reducing this response time by minimizing offloading, and pushing computation close to the source of the data, i.e. to edge servers and devices themselves. To adapt to the resource constrained environment at the edge, it presents an approach that leverages spatiotemporal locality to push subparts of the model to the edge. This approach is embodied in a distributed caching framework, Cachier. Cachier is built upon a novel caching model for recognition, and is distributed across edge servers and devices. The analytical caching model for recognition provides a formulation for expected latency for recognition requests in Cachier. The formulation incorporates the effects of compute time and accuracy. It also incorporates network conditions, thus providing a method to compute expected response times under various conditions. This is utilized as a cost function by Cachier, at edge servers and devices. By analyzing requests at the edge server, Cachier caches relevant parts of the trained model at edge servers, which is used to respond to requests, minimizing the number of requests that go to the cloud. Then, Cachier uses context-aware prediction to prefetch parts of the trained model onto devices. The requests can then be processed on the devices, thus minimizing the number of offloaded requests. Finally, Cachier enables cooperation between nearby devices to allow exchanging prefetched data, reducing the dependence on remote servers even further. The efficacy of Cachier is evaluated by using it with an art recognition application. The application is driven using real world traces gathered at museums. By conducting a large-scale study with different control variables, we show that Cachier can lower latency, increase scalability and decrease infrastructure resource usage, while maintaining high accuracy.
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Yin, Wenjie. "Machine Learning for Adaptive Cruise Control Target Selection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264918.

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Vehicles will be more complex, safe, and intelligent in the future. For instance, with the support of the advanced driver assistance system (ADAS), the safety and comfort of the driver and the passengers can be significantly improved. This degree project proposes data-driven solutions for adaptive cruise control (ACC) target selection that can be used to select one of the preceding vehicles as the primary target that similar to the choice of human drivers. This master degree project was carried out at Scania CV AB. A shared-network and a shared-LSTM network were used to select the primary target. Besides, A novel machine learning based target selection model (compare-target model) was designed, which can consider all neighboring vehicles together by comparing vehicles. A compare-target network and a compare-target XGBoost are developed based on the compare-target model. In total, four different machine learning methods were adopted to select the primary target for ACC, includ- ing a shared network, a shared-LSTM network, a compare-target network, and a compare-target XGBoost model. These methods were compared and analyzed. Fine-tuning was adopted to overcome the data imbalance problem of rare situations. The compare-target XGBoost can achieve 94.85% accuracy on the test set.
Fordon kommer att vara mer komplexa, säkra och intelligenta i framtiden. Till exempel, med stöd av det avancerade förarassistanssystemet (ADAS) kan föraren och passagerarnas säkerhet och komfort förbättras avsevärt. Detta examensarbete föreslår datastyrda lösningar för målval för adaptivt fartreglering (ACC) för att välja ett av föregående fordon som det primära målet. Valet liknar det som människor gör. Arbetet genomfördes i samarbete med Scania CV AB. Ett delat nätverk och ett gemensamt LSTM-nätverk användes för att välja det primära målet. Dessutom har en ny maskinbaserad målvalsmodell (jämförelse-målmodell) utformats, vilken kan överväga alla närliggande fordon tillsammans genom att jämföra fordon. Ett jämför-mål-nätverk och ett jämförbart mål XGBoost utvecklas baserat på jämförelsemodellen. Totalt användes fyra olika maskininlärningsmetoder för att välja det primara målet för ACC, inklusive ett delat nätverk, ett gemensamt LSTM-nätverk, ett jämförelsemål-nätverk och en jämförbar XGBoost-modell. Dessa meto- der jämfördes och analyserades. Finjustering antogs för att motverka dataobalansproblemet för sällsynta situationer. Jämförelse-målet XGBoost kan uppnå 94.85% noggrannhet på testuppsättningen.
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Grundtman, Per. "Adaptive Learning." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61648.

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The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance.
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Books on the topic "Adaptive machine learning"

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Chatterjee, Chanchal. Adaptive Machine Learning Algorithms with Python. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8017-1.

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Lopes, Noel, and Bernardete Ribeiro. Machine Learning for Adaptive Many-Core Machines - A Practical Approach. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06938-8.

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Bhanu, Bir. Genetic learning for adaptive image segmentation. Boston: Kluwer Academic Publishers, 1994.

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He, Haibo. Self-adaptive systems for machine intelligence. Hoboken, N.J: Wiley-Interscience, 2011.

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Whiteson, Shimon. Adaptive representations for reinforcement learning. Berlin: Springer Verlag, 2010.

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Gori͡achkin, O. V. Metody slepoĭ obrabotki signalov i ikh prilozhenii͡a v sistemakh radiotekhniki i svi͡azi. Moskva: Radio i svi͡azʹ, 2003.

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Wei, Chia-Hung. Machine learning techniques for adaptive multimedia retrieval: Technologies, applications, and perspectives. Hershey, PA: Information Science Reference, 2011.

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1938-, Mendel Jerry M., ed. A Prelude to neural networks: Adaptive and learning systems. Englewood Cliffs, N.J: PTR Prentice Hall, 1994.

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1975-, Butz Martin V., and International Conference on Simulation of Adaptive Behavior (9th : 2006 : Rome, Italy), eds. Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior. Berlin: Springer, 2007.

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Cichocki, Andrzej. Adaptive blind signal and image processing: Learning algorithms and applications. Chichester: J. Wiley, 2002.

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Book chapters on the topic "Adaptive machine learning"

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Paluszek, Michael, and Stephanie Thomas. "Adaptive Control." In MATLAB Machine Learning, 207–68. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2250-8_11.

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Paluszek, Michael, and Stephanie Thomas. "Adaptive Control." In MATLAB Machine Learning Recipes, 109–33. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3916-2_5.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive System." In Encyclopedia of Machine Learning, 35. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_12.

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Golden, Richard M. "Adaptive Learning Algorithm Convergence." In Statistical Machine Learning, 325–58. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-12.

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van Someren, M., and S. ten Hagen. "Machine Learning and Reinforcement Learning." In Do Smart Adaptive Systems Exist?, 81–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-32374-0_5.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Resonance Theory." In Encyclopedia of Machine Learning, 22–35. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_11.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Complex Adaptive System." In Encyclopedia of Machine Learning, 194. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_147.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Control Processes." In Encyclopedia of Machine Learning, 19. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_9.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Complexity in Adaptive Systems." In Encyclopedia of Machine Learning, 194–98. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_148.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Adaptive Real-Time Dynamic Programming." In Encyclopedia of Machine Learning, 19–22. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_10.

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Conference papers on the topic "Adaptive machine learning"

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Wong, Alison, Barnaby R. M. Norris, Vincent Deo, Olivier Guyon, Peter G. Tuthill, Julien Lozi, Sébastien Vievard, and Kyohoon Ahn. "Machine learning for wavefront sensing." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2628869.

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Rossi, Fabio, Cédric Plantet, Anne-Laure Lucie Ceffot, Guido Agapito, Enrico Pinna, and Simone Esposito. "Machine learning techniques for piston sensing." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2629983.

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Mehanna, Ryan. "Resilient Structures Through Machine Learning And Evolution." In ACADIA 2013: Adaptive Architecture. ACADIA, 2013. http://dx.doi.org/10.52842/conf.acadia.2013.319.

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Oboko, Robert O., Elizaphan M. Maina, Peter W. Waiganjo, Elijah I. Omwenga, and Ruth D. Wario. "Designing adaptive learning support through machine learning techniques." In 2016 IST-Africa Week Conference. IEEE, 2016. http://dx.doi.org/10.1109/istafrica.2016.7530676.

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Hentschel, Alexander, and Barry C. Sanders. "Machine Learning for Adaptive Quantum Measurement." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.109.

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Brockmann, W. "Online Machine Learning For Adaptive Control." In IEEE International Workshop on Emerging Technologies and Factory Automation,. IEEE, 1992. http://dx.doi.org/10.1109/etfa.1992.683251.

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Frosio, Iuri, Karen Egiazarian, and Kari Pulli. "Machine learning for adaptive bilateral filtering." In IS&T/SPIE Electronic Imaging, edited by Karen O. Egiazarian, Sos S. Agaian, and Atanas P. Gotchev. SPIE, 2015. http://dx.doi.org/10.1117/12.2077733.

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Turchi, Alessio, Elena Masciadri, and Luca Fini. "Optical turbulence forecast over short timescales using machine learning techniques." In Adaptive Optics Systems VIII, edited by Dirk Schmidt, Laura Schreiber, and Elise Vernet. SPIE, 2022. http://dx.doi.org/10.1117/12.2629501.

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Zhou, Tao, Yan-Ning Zhang, He-Jing Yuan, and Hui-Ling Lu. "Rough k-means Cluster with Adaptive Parameters." In Sixth International Conference on Machine Learning Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370674.

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Lee, C. S., and A. Elgammal. "Style Adaptive Bayesian Tracking Using Explicit Manifold Learning." In British Machine Vision Conference 2005. British Machine Vision Association, 2005. http://dx.doi.org/10.5244/c.19.55.

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Reports on the topic "Adaptive machine learning"

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Gur, Ilan. Real-Time Adaptive Machine Learning Platform. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1607789.

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Xu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada588768.

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Mueller, Juliane, Charuleka Varadharajan, Erica Siirila-Woodburn, and Charles Koven. Machine Learning for Adaptive Model Refinement to Bridge Scales. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769741.

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Comstock, Jennifer, Joseph Hardin, and Zhe Feng. Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769712.

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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Abstract:
Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Scheinker, Alexander, and Reeju Pokharel. Adaptive Machine Learning for Bragg Coherent Diffraction Imaging (BCDI) of 3D Electron Density Maps with Application to La2-xBaxCuO4 (LBCO) High Temperature Superconductor Studies. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1671079.

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Berry, Jonathan, Anand Ganti, Kenneth Goss, Carolyn Mayer, Uzoma Onunkwo, Cynthia Phillips, Jared Saia, and Timothy Shead. Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1842271.

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