The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatic...
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
The maximum number of points that can be shattered by H is called the Vapnik-Chervonenkis (VC) dimension of H , is denoted as VC(H ), and measures the capacity of H (查看原文)
In real life, the world is smoothly changing, instances close by most of the time have the same labels, and we need not worry about all possible labelings. (查看原文)
Adaptive Computation and Machine Learning(共34册)，
这套丛书还有
《Introduction to Natural Language Processing》《Machine Learning from Weak Supervision》《Causation, Prediction and Search》《Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)》《Foundations of Computer Vision》
等
。
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机械工业出版社2014版
子莒
0 有用 陈永仁 2014-05-16 20:57:57
真的只是入门
0 有用 pashaying 2012-02-27 21:12:08
理论推导十分详尽
0 有用 陈永仁 2014-05-16 20:57:57
真的只是入门
0 有用 pashaying 2012-02-27 21:12:08
理论推导十分详尽