000			03030cam a2200277 a 4500
008			090302s2004 maua b 001 0 eng
010			_a2004109627
020			_a9780262012119
035			_a(Sirsi) u1158
040			_aEG-CaNU _cEG-CaNU _dEG-CaNU
042			_ancode
082	0	0	_a006.31 _2 22
100	1		_aAlpaydin, Ethem. _9641
245	1	0	_aIntroduction to machine learning / _c Ethem Alpayd?n.
260			_aCambridge, Mass. : _b MIT Press, _c c2004.
300			_axxx, 415 p. : _b ill. ; _c 24 cm.
490	0		_aAdaptive computation and machine learning
504			_aIncludes bibliographical references and index.
505	0		_a1 Introduction -- 2 Supervised Learning -- Bayesian Decision Theory -- 4 Parametric Methods -- 5 Multivariate Methods -- 6 Dimensionality Reduction -- 7 Clustering -- 8 Nonparametric Methods -- 9 Decision Trees -- 10 Linear Discrimination -- 11 Multilayer Perceptrons -- 12 Local Models -- 13 Hidden Markov Models -- 14 Assessing and Comparing Classification Algorithms -- 15 Combining Multiple Learners -- 16 Reinforcement Learning.
520			_aThe 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, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. 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. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book 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. After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
630	0	0	_aCIT. _914
650		7	_aAprendizado computacional. _2 larpcal _9642
650		0	_aMachine learning. _9107
650		6	_aApprentissage automatique. _9643
596			_a1
999			_c182 _d182