The Unified Learning Model
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The Unified Learning Model
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Author : Duane F. Shell
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-03
The Unified Learning Model written by Duane F. Shell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-03 with Education categories.
This is a book about how humans learn. Our focus is on classroom learning although the principles are, as the name of this book indicates, universal. We are concerned with learning from pre-school to post-graduate. We are concerned with most bu- ness, industrial and military training. We do not address how infants learn how to speak or walk, or how grown-ups improve their tennis swing. We do address all learning described by the word “thought”, as well as anything we might try to teach, or instruct in formal educational settings. In education, the words theory and model imply conjecture. In science, these same words imply something that is a testable explanation of phenomena able to predict outcomes of experiments. This book presents a model of learning that the authors offer in the sense of scientists rather than educators. Conjecture implies that information is incomplete, and so it surely is with human learning. On the other hand, we assert that more than enough is known to sustain a “scienti?c” model of learning. This book is not a review of the literature. Instead, it is a synthesis. Scholars and many teachers likely have heard much if not most or even all of the information we use to develop the uni?ed learning model. What you have not read before is a model putting the information together in just this way; this is the ?rst one.
Statistical Machine Learning
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Author : Richard Golden
language : en
Publisher: CRC Press
Release Date : 2020-06-24
Statistical Machine Learning written by Richard Golden and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Computers categories.
The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
Unified Modeling Language Systems Analysis Design And Development Issues
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Author : Siau, Keng
language : en
Publisher: IGI Global
Release Date : 2000-07-01
Unified Modeling Language Systems Analysis Design And Development Issues written by Siau, Keng and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-07-01 with Computers categories.
UML is a large and complex language, with many features in need of refinement or clarification, and there are different views about how to use UML to build systems. This book sheds light on such issues, by illustrating how UML can be used successfully in practice as well as identifying various problematic aspects of UML and suggesting possible solutions.
Theoretical And Computational Models Of Word Learning Trends In Psychology And Artificial Intelligence
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Author : Gogate, Lakshmi
language : en
Publisher: IGI Global
Release Date : 2013-02-28
Theoretical And Computational Models Of Word Learning Trends In Psychology And Artificial Intelligence written by Gogate, Lakshmi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-28 with Computers categories.
The process of learning words and languages may seem like an instinctual trait, inherent to nearly all humans from a young age. However, a vast range of complex research and information exists in detailing the complexities of the process of word learning. Theoretical and Computational Models of Word Learning: Trends in Psychology and Artificial Intelligence strives to combine cross-disciplinary research into one comprehensive volume to help readers gain a fuller understanding of the developmental processes and influences that makeup the progression of word learning. Blending together developmental psychology and artificial intelligence, this publication is intended for researchers, practitioners, and educators who are interested in language learning and its development as well as computational models formed from these specific areas of research.
Advances In Neural Information Processing Systems 8
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Author : David S. Touretzky
language : en
Publisher: MIT Press
Release Date : 1996
Advances In Neural Information Processing Systems 8 written by David S. Touretzky and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book
Learning Representation For Multi View Data Analysis
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Author : Zhengming Ding
language : en
Publisher: Springer
Release Date : 2018-12-06
Learning Representation For Multi View Data Analysis written by Zhengming Ding and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-06 with Computers categories.
This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal. A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Representation Learning For Natural Language Processing
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2023-08-23
Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-23 with Computers categories.
This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.
An Evidence Based Guide To College And University Teaching
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Author : Aaron S. Richmond
language : en
Publisher: Routledge
Release Date : 2021-11-29
An Evidence Based Guide To College And University Teaching written by Aaron S. Richmond and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Psychology categories.
An Evidence-based Guide to College and University Teaching outlines a definition of "model teaching" based on research evidence and accepted best practices in high education. Teachers at all levels of skill and experience can benefit from clear, objective guidelines for defining and measuring quality teaching. To fulfil this need, this book outlines six fundamental areas of teaching competency—model teaching characteristics—and provides detailed definitions of each characteristic. The authors define these essential characteristics as training, course content, the assessment process, instructional methods, syllabus construction, and the use of student evaluations. This guide outlines through research and supplemental evidence how each characteristic can be used toward tenure, promotion, teaching portfolios, and general professional development. Additional features include a self-assessment tool that corresponds to the model teaching characteristics, case studies illustrating common teaching problems, and lists of "must reads" about college teaching. An Evidence-based Guide to College and University Teaching describes how college faculty from all disciplines and at all levels of their career – from graduate students to late-career faculty – can use the model teaching characteristics to evaluate, guide, and improve their teaching. The book is additionally useful for teachers, trainers, and administrators responsible for promoting excellence in college teaching.
Towards A Unified Modeling And Knowledge Representation Based On Lattice Theory
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Author : Vassilis G. Kaburlasos
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-02-07
Towards A Unified Modeling And Knowledge Representation Based On Lattice Theory written by Vassilis G. Kaburlasos and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-02-07 with Computers categories.
This research monograph proposes a unified, cross-fertilizing approach for knowledge-representation and modeling based on lattice theory. The emphasis is on clustering, classification, and regression applications. It presents novel tools and useful perspectives for effective pattern classification. The material is multi-disciplinary based on on-going research published in major scientific journals and conferences.
Unified Computational Intelligence For Complex Systems
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Author : John Seiffertt
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-15
Unified Computational Intelligence For Complex Systems written by John Seiffertt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-15 with Computers categories.
Computational intelligence encompasses a wide variety of techniques that allow computation to learn, to adapt, and to seek. That is, they may be designed to learn information without explicit programming regarding the nature of the content to be retained, they may be imbued with the functionality to adapt to maintain their course within a complex and unpredictably changing environment, and they may help us seek out truths about our own dynamics and lives through their inclusion in complex system modeling. These capabilities place our ability to compute in a category apart from our ability to erect suspension bridges, although both are products of technological advancement and reflect an increased understanding of our world. In this book, we show how to unify aspects of learning and adaptation within the computational intelligence framework. While a number of algorithms exist that fall under the umbrella of computational intelligence, with new ones added every year, all of them focus on the capabilities of learning, adapting, and helping us seek. So, the term unified computational intelligence relates not to the individual algorithms but to the underlying goals driving them. This book focuses on the computational intelligence areas of neural networks and dynamic programming, showing how to unify aspects of these areas to create new, more powerful, computational intelligence architectures to apply to new problem domains.