Computational Learning Theory And Natural Learning Systems
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Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1995
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computational learning theory categories.
Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1994
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Machine learning categories.
Computational Learning Theory And Natural Learning Systems Selecting Good Models
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Author : Stephen José Hanson
language : en
Publisher: Bradford Books
Release Date : 1994
Computational Learning Theory And Natural Learning Systems Selecting Good Models written by Stephen José Hanson and has been published by Bradford Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.
Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19
Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Computational Learning Theory And Natural Learning Systems Intersections Between Theory And Experiment
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Author : George A. Drastal
language : en
Publisher:
Release Date : 1994
Computational Learning Theory And Natural Learning Systems Intersections Between Theory And Experiment written by George A. Drastal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computational learning theory categories.
An Introduction To Computational Learning Theory
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Author : Michael J. Kearns
language : en
Publisher: MIT Press
Release Date : 1994-08-15
An Introduction To Computational Learning Theory written by Michael J. Kearns and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-08-15 with Computers categories.
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Machine Learning
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Author : RODRIGO F MELLO
language : en
Publisher: Springer
Release Date : 2018-08-01
Machine Learning written by RODRIGO F MELLO and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-01 with Computers categories.
This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
Computational Learning Theory And Natural Learning Systems
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Author : Stephen José Hanson
language : en
Publisher:
Release Date : 1994
Computational Learning Theory And Natural Learning Systems written by Stephen José Hanson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with categories.
Computational Learning Theory
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Author : Shai Ben-David
language : en
Publisher: Springer Science & Business Media
Release Date : 1997-03-03
Computational Learning Theory written by Shai Ben-David 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 1997-03-03 with Computers categories.
Content Description #Includes bibliographical references and index.
Computational Learning Theory And Natural Learning Systems
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Author : Thomas Petsche
language : en
Publisher:
Release Date : 1995
Computational Learning Theory And Natural Learning Systems written by Thomas Petsche and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Machine learning categories.