Empirical Approach To Machine Learning

Download Empirical Approach To Machine Learning full books in PDF, epub, and Kindle. Read online Empirical Approach To Machine Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Empirical Approach to Machine Learning

Author : Plamen P. Angelov
Publisher : Springer
ISBN 13 : 3030023842
Total Pages : 423 pages
Book Rating : 4.0/5 (3 download)

DOWNLOAD NOW!

Book Synopsis Empirical Approach to Machine Learning by : Plamen P. Angelov

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 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.”

Empirical Methods for Artificial Intelligence

Author : Paul R. Cohen
Publisher : Bradford Books
ISBN 13 : 9780262032254
Total Pages : 405 pages
Book Rating : 4.0/5 (322 download)

DOWNLOAD NOW!

Book Synopsis Empirical Methods for Artificial Intelligence by : Paul R. Cohen

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.

Empirical Asset Pricing

Author : Wayne Ferson
Publisher : MIT Press
ISBN 13 : 0262039370
Total Pages : 497 pages
Book Rating : 4.2/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Empirical Asset Pricing by : Wayne Ferson

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.

Validity, Reliability, and Significance

Author : Stefan Riezler
Publisher : Springer Nature
ISBN 13 : 3031021835
Total Pages : 147 pages
Book Rating : 4.0/5 (31 download)

DOWNLOAD NOW!

Book Synopsis Validity, Reliability, and Significance by : Stefan Riezler

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.

Empirical Methods in Natural Language Generation

Author : Emiel Krahmer
Publisher : Springer Science & Business Media
ISBN 13 : 3642155723
Total Pages : 363 pages
Book Rating : 4.6/5 (421 download)

DOWNLOAD NOW!

Book Synopsis Empirical Methods in Natural Language Generation by : Emiel Krahmer

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.

Memory in the Cerebral Cortex

Author : Joaquin M. Fuster
Publisher : Bradford Books
ISBN 13 : 9780262561242
Total Pages : 358 pages
Book Rating : 4.5/5 (612 download)

DOWNLOAD NOW!

Book Synopsis Memory in the Cerebral Cortex by : Joaquin M. Fuster

Download or read book Memory in the Cerebral Cortex written by Joaquin M. Fuster and published by Bradford Books. This book was released on 1999 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Joaquín M. Fuster presents the insights of more than three decades of empirical research on the neural processes by which memory is formed, stored, and retrieved. In Memory in the Cerebral Cortex, Joaquín M. Fuster presents the insights of more than three decades of empirical research on the neural processes by which memory is formed, stored, and retrieved. Spanning the field from neuroanatomy to modeling, this book brings together all that we presently know about the role of the cerebral cortex of the primate in memory.

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

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.

Machine Learning

Author : Yves Kodratoff
Publisher : Elsevier
ISBN 13 : 0080510558
Total Pages : 836 pages
Book Rating : 4.0/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning by : Yves Kodratoff

Download or read book Machine Learning written by Yves Kodratoff and published by Elsevier. This book was released on 2014-06-28 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.

Computational Intelligence Techniques and Their Applications to Software Engineering Problems

Author : Ankita Bansal
Publisher : CRC Press
ISBN 13 : 100019194X
Total Pages : 809 pages
Book Rating : 4.0/5 (1 download)

DOWNLOAD NOW!

Book Synopsis Computational Intelligence Techniques and Their Applications to Software Engineering Problems by : Ankita Bansal

Download or read book Computational Intelligence Techniques and Their Applications to Software Engineering Problems written by Ankita Bansal and published by CRC Press. This book was released on 2020-09-27 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

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.

Moral Uncertainty

Author : William MacAskill
Publisher : Oxford University Press
ISBN 13 : 0198722273
Total Pages : 237 pages
Book Rating : 4.1/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Moral Uncertainty by : William MacAskill

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.

Machine Learning from Weak Supervision

Author : Masashi Sugiyama
Publisher : MIT Press
ISBN 13 : 0262047071
Total Pages : 315 pages
Book Rating : 4.2/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning from Weak Supervision by : Masashi Sugiyama

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.

Foundations of Machine Learning, second edition

Author : Mehryar Mohri
Publisher : MIT Press
ISBN 13 : 0262351366
Total Pages : 505 pages
Book Rating : 4.2/5 (623 download)

DOWNLOAD NOW!

Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri

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.

Ethics, Technology, and Engineering

Author : Ibo van de Poel
Publisher : John Wiley & Sons
ISBN 13 : 1444330950
Total Pages : 390 pages
Book Rating : 4.4/5 (443 download)

DOWNLOAD NOW!

Book Synopsis Ethics, Technology, and Engineering by : Ibo van de Poel

Download or read book Ethics, Technology, and Engineering written by Ibo van de Poel and published by John Wiley & Sons. This book was released on 2011-05-02 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Featuring a wide range of international case studies, Ethics, Technology, and Engineering presents a unique and systematic approach for engineering students to deal with the ethical issues that are increasingly inherent in engineering practice. Utilizes a systematic approach to ethical case analysis -- the ethical cycle -- which features a wide range of real-life international case studies including the Challenger Space Shuttle, the Herald of Free Enterprise and biofuels. Covers a broad range of topics, including ethics in design, risks, responsibility, sustainability, and emerging technologies Can be used in conjunction with the online ethics tool Agora (http://www.ethicsandtechnology.com) Provides engineering students with a clear introduction to the main ethical theories Includes an extensive glossary with key terms

Empirical Methods for Artificial Intelligence

Author : Paul R Cohen
Publisher :
ISBN 13 : 9780262534178
Total Pages : 422 pages
Book Rating : 4.5/5 (341 download)

DOWNLOAD NOW!

Book Synopsis Empirical Methods for Artificial Intelligence by : Paul R Cohen

Download or read book Empirical Methods for Artificial Intelligence written by Paul R Cohen and published by . This book was released on 2017-05-26 with total page 422 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.

Machine Learning for Economics and Finance in TensorFlow 2

Author : Isaiah Hull
Publisher : Apress
ISBN 13 : 9781484263723
Total Pages : 368 pages
Book Rating : 4.2/5 (637 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning for Economics and Finance in TensorFlow 2 by : Isaiah Hull

Download or read book Machine Learning for Economics and Finance in TensorFlow 2 written by Isaiah Hull and published by Apress. This book was released on 2020-11-26 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.

Agile Processes in Software Engineering and Extreme Programming

Author : Philippe Kruchten
Publisher : Springer
ISBN 13 : 9783030190330
Total Pages : 0 pages
Book Rating : 4.1/5 (93 download)

DOWNLOAD NOW!

Book Synopsis Agile Processes in Software Engineering and Extreme Programming by : Philippe Kruchten

Download or read book Agile Processes in Software Engineering and Extreme Programming written by Philippe Kruchten and published by Springer. This book was released on 2019-04-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the proceedings of the 20th International Conference on Agile Software Development, XP 2019, held in Montreal, QC, Canada, in May 2019. XP is the premier agile software development conference combining research and practice. It is a hybrid forum where agile researchers, academics, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. Following this history, for both researchers and seasoned practitioners XP 2019 provided an informal environment to network, share, and discover trends in Agile for the next 20 years The 15 full papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections named: agile adoption, agile practices; large-scale agile; agility beyond IT, and the future of agile.