Download or read book Empirical Approach to Machine Learning written by Plamen P. Angelov and published by Springer. This book was released on 2018-12-13 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.” Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.” Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”

Download or read book Empirical Approach to Machine Learning written by Plamen P. Angelov and published by Springer. This book was released on 2018-10-17 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.” Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.” Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”

Download or read book Empirical Methods for Artificial Intelligence written by Paul R. Cohen and published by Bradford Books. This book was released on 1995 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data.

Download or read book Validity Reliability and Significance written by Stefan Riezler and published by Springer Nature. This book was released on 2022-06-01 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity, reliability, and significance. In the case of machine learning, these correspond to the questions of whether a model predicts what it purports to predict, whether a model's performance is consistent across replications, and whether a performance difference between two models is due to chance, respectively. The goal of this book is to answer these questions by concrete statistical tests that can be applied to assess validity, reliability, and significance of data annotation and machine learning prediction in the fields of NLP and data science. Our focus is on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows detecting circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Last, a significance test based on the likelihood ratio of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. This book can be used as an introduction to empirical methods for machine learning in general, with a special focus on applications in NLP and data science. The book is self-contained, with an appendix on the mathematical background on GAMs and LMEMs, and with an accompanying webpage including R code to replicate experiments presented in the book.

Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Download or read book Empirical Methods in Natural Language Generation written by Emiel Krahmer and published by Springer Science & Business Media. This book was released on 2010-09-09 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.

Download or read book Empirical Evaluation Methods in Computer Vision written by Henrik I. Christensen and published by World Scientific. This book was released on 2002 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms.

Download or read book Machine Learning from Weak Supervision written by Masashi Sugiyama and published by MIT Press. This book was released on 2022-08-23 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom. The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation.

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Download or read book Surprising Empirical Phenomena of Deep Learning and Kernel Machines written by Like Hui and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past decade, the field of machine learning has witnessed significant advancements in artificial intelligence, primarily driven by empirical research. Within this context, we present various surprising empirical phenomena observed in deep learning and kernel machines. Among the crucial components of a learning system, the training objective holds immense importance. In the realm of classification tasks, the cross-entropy loss has emerged as the dominant choice for training modern neural architectures, widely believed to offer empirical superiority over the square loss. However, limited compelling empirical or theoretical evidence exists to firmly establish the clear-cut advantage of the cross-entropy loss. In fact, our findings demonstrate that training with the square loss achieves comparable or even better results than the cross-entropy loss, even when computational resources are equalized. However, it remains unclear how the rescaling hyperparameter R, needs to vary with the number of classes. We provide an exact analysis for a 1-layer ReLU network in the proportional asymptotic regime for isotropic Gaussian data. Specifically, we focus on the optimal choice of R as a function of (i) the number of classes, (ii) the degree of overparameterization, and (iii) the level of label noise. Finally, we provide empirical results on real data, which supports our theoretical predictions. Afterwards, to avoid extra parameters brought by the rescaling of the square loss (in cases when class number is large), later on we propose the "squentropy" loss, which is the sum of the cross-entropy loss and the average square loss over the incorrect classes. We show that the squentropy loss outperforms both the pure cross entropy and rescaled square losses interms of the classification accuracy and model calibration. Also, squentropy loss is a simple "plug-and-play" replacement of cross-entropy as it requires no extra hyperparameters and no extra tuning on optimization parameters. Also, we apply theoretically well-understood kernel machines to practical challenging tasks, speech enhancement, and found that kernel machines actually outperform fully connected networks and require less computation resources. In another work, we investigate the correlation between the Neural Collapse phenomenon proposed by Papyan, Han, & Donoho (2020) and generalization in deep learning. We give precise definitions and their corresponding feasibility on generalization, which clarify neural collapse concepts. Moreover, our empirical evidence supports our claim that neural collapse is mainly an optimization phenomenon.

Download or read book Foundations of Machine Learning second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Download or read book Moral Uncertainty written by William MacAskill and published by Oxford University Press. This book was released on 2020 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: About the bookToby Ord try to fill this gap. They argue that there are distinctive norms that govern how one ought to make decisions and defend an information-sensitive account of how to make such decisions. They do so by developing an analogy between moral uncertainty and social choice, noting that different moral views provide different amounts of information regarding our reasons for action, and arguing that the correct account of decision-making under moral uncertainty must be sensitive to that. Moral Uncertainty also tackles the problem of how to make intertheoretic comparisons, and addresses the implications of their view for metaethics and practical ethics. Very often we are uncertain about what we ought, morally, to do. We do not know how to weigh the interests of animals against humans, how strong our duties are to improve the lives of distant strangers, or how to think about the ethics of bringing new people into existence. But we still need to act. So how should we make decisions in the face of such uncertainty? Though economists and philosophers have extensively studied the issue of decision-making in the face of uncertainty about matters of fact, the question of decision-making given fundamental moral uncertainty has been neglected. In Moral Uncertainty, philosophers William MacAskill, Krister Bykvist, and Toby Ord try to fill this gap. They argue that there are distinctive norms that govern how one ought to make decisions and defend an information-sensitive account of how to make such decisions. They do so by developing an analogy between moral uncertainty and social choice, noting that different moral views provide different amounts of information regarding our reasons for action, and arguing that the correct account of decision-making under moral uncertainty must be sensitive to that. Moral Uncertainty also tackles the problem of how to make intertheoretic comparisons, and addresses the implications of their view for metaethics and practical ethics.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Download or read book Creating A Memory of Causal Relationships written by Michael J. Pazzani and published by Psychology Press. This book was released on 2014-02-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.

Download or read book Empirical Inference written by Bernhard Schölkopf and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.

Download or read book Extending Explanation Based Learning by Generalizing the Structure of Explanations written by Jude W. Shavlik and published by Morgan Kaufmann. This book was released on 2014-07-10 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning.

Download or read book Textbook of Machine Learning and Data Mining written by Hiroshi Mamitsuka and published by . This book was released on 2018-09-12 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven approaches, particularly machine learning and data mining, are the main driving force of the current artificial intelligence technology. This book covers a wide variety of methods in machine learning and data mining, dividing them from a viewpoint of data types, which begin with rather simple vectors and end by graphs and also combination of different data types. This book describes standard techniques of machine learning and data mining for each data type, especially focusing on the relevance and difference among them. Also after explaining a series of machine learning methods for seven different data types, this book has a chapter for standard validation methods on empirical results obtained by applying machine learning methods to data. This book can be used for a variety of objectives, including an introductory textbook of studying machine learning and a (first step) book to start machine learning research, etc.