Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

ISBN: 0521518148

Category: Computers

Page: 697

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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
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Fundamentals of Machine Learning for Predictive Data Analytics

Algorithms, Worked Examples, and Case Studies

Author: John D. Kelleher,Brian Mac Namee,Aoife D'Arcy

Publisher: MIT Press

ISBN: 0262029448

Category: Computers

Page: 624

View: 2212

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A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
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Machine Learning

A Bayesian and Optimization Perspective

Author: Sergios Theodoridis

Publisher: Academic Press

ISBN: 0128188049

Category: Computers

Page: 1160

View: 1879

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Machine Learning: A Bayesian and Optimization Perspective, Second Edition, gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches based on optimization techniques combined with the Bayesian inference approach. The book builds from the basic classical methods to recent trends, making it suitable for different courses, including pattern recognition, statistical/adaptive signal processing, and statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. In addition, sections cover major machine learning methods developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth and supported by examples and problems, giving an invaluable resource to both the student and researcher for understanding and applying machine learning concepts. This updated edition includes many more simple examples on basic theory, complete rewrites of the chapter on Neural Networks and Deep Learning, and expanded treatment of Bayesian learning, including Nonparametric Bayesian Learning. Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more
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Statistical Relational Artificial Intelligence

Logic, Probability, and Computation

Author: Luc De Raedt,Kristian Kersting,Sriraam Natarajan,David Poole

Publisher: Morgan & Claypool Publishers

ISBN: 1681731800

Category: Computers

Page: 189

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An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
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Bayesian Methods for Nonlinear Classification and Regression

Author: David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith

Publisher: John Wiley & Sons

ISBN: 9780471490364

Category: Mathematics

Page: 296

View: 1912

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Nonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest. Nonlinear models offer more flexibility than those with linear assumptions, and their implementation has now become much easier due to increases in computational power. Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference. Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches. * Demonstrates how Bayesian ideas can be used to improve existing statistical methods. * Includes coverage of Bayesian additive models, decision trees, nearest-neighbour, wavelets, regression splines, and neural networks. * Emphasis is placed on sound implementation of nonlinear models. * Discusses medical, spatial, and economic applications. * Includes problems at the end of most of the chapters. * Supported by a web site featuring implementation code and data sets. Primarily of interest to researchers of nonlinear statistical modelling, the book will also be suitable for graduate students of statistics. The book will benefit researchers involved inregression and classification modelling from electrical engineering, economics, machine learning and computer science.
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Machine Learning and Its Applications

Advanced Lectures

Author: Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos

Publisher: Springer

ISBN: 3540446737

Category: Computers

Page: 324

View: 6982

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In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
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Computational Learning and Probabilistic Reasoning

Author: A. Gammerman

Publisher: Wiley

ISBN: 9780471962793

Category: Computers

Page: 338

View: 7433

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Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.
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Symbolic and Quantitative Approaches to Reasoning with Uncertainty

9th European Conference, ECSQARU 2007, Hammamet, Tunisia, October 31 - November 2, 2007, Proceedings

Author: Khaled Mellouli

Publisher: Springer Science & Business Media

ISBN: 3540752552

Category: Computers

Page: 914

View: 3295

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This book constitutes the refereed proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2007, held in Hammammet, Tunisia, Oktober 31 - November 2, 2007. The 78 revised full papers presented together with 3 invited papers were carefully reviewed and selected from over hundret submissions for inclusion in the book. The papers are organized in topical sections on Bayesian networks, graphical models, learning causal networks, planning, causality and independence, preference modelling and decision, argumentation systems, inconsistency handling, belief revision and merging, belief functions, fuzzy models, many-valued logical systems, uncertainty logics, probabilistic reasoning, reasoning models under uncertainty, uncertainty measures, probabilistic classifiers, classification and clustering, and industrial applications.
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Probabilistic Graphical Models

Principles and Techniques

Author: Daphne Koller,Nir Friedman,Francis Bach

Publisher: MIT Press

ISBN: 0262013193

Category: Computers

Page: 1231

View: 5381

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Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present. Since 1985, the Conference on Uncertainty in Artificial Intelligence (UAI) has been the primary international forum for exchanging results on the use of principled uncertain-reasoning methods in intelligent systems. The UAI Proceedings have become a basic reference for researches and practitioners who want to know about both theoretical advances and the latest applied developments in the field.
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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010. Proceedings

Author: José L. Balcázar,Francesco Bonchi,Aristides Gionis,Michèle Sebag

Publisher: Springer

ISBN: 3642158838

Category: Computers

Page: 518

View: 7789

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The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010, was held in Barcelona, September 20–24, 2010, consolidating the long junction between the European Conference on Machine Learning (of which the ?rst instance as European wo- shop dates back to 1986) and Principles and Practice of Knowledge Discovery in Data Bases (of which the ?rst instance dates back to 1997). Since the two conferences were ?rst collocated in 2001, both machine learning and data m- ing communities have realized how each discipline bene?ts from the advances, and participates to de?ning the challenges, of the sister discipline. Accordingly, a single ECML PKDD Steering Committee gathering senior members of both communities was appointed in 2008. In 2010, as in previous years, ECML PKDD lasted from Monday to F- day. It involved six plenary invited talks, by Christos Faloutsos, Jiawei Han, Hod Lipson, Leslie Pack Kaelbling, Tomaso Poggio, and Jur ̈ gen Schmidhuber, respectively. Monday and Friday were devoted to workshops and tutorials, or- nized and selected by Colin de la Higuera and Gemma Garriga.Continuing from ECML PKDD 2009, an industrial session managed by Taneli Mielikainen and Hugo Zaragoza welcomed distinguished speakers from the ML and DM ind- try: Rakesh Agrawal, Mayank Bawa, Ignasi Belda, Michael Berthold, Jos ́eLuis Fl ́ orez, ThoreGraepel, andAlejandroJaimes.Theconferencealsofeaturedad- coverychallenge, organizedbyAndr ́ asBenczur ́, CarlosCastillo, Zolt ́ anGyon ̈ gyi, and Julien Masan' es.
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