LEADER 04647nam 2200589 450 001 9910583088403321 005 20200423115102.0 010 $a0-12-802350-3 035 $a(CKB)3710000000527103 035 $a(EBL)4107705 035 $a(SSID)ssj0001682937 035 $a(PQKBManifestationID)16508413 035 $a(PQKBTitleCode)TC0001682937 035 $a(PQKBWorkID)15037241 035 $a(PQKB)11152561 035 $a(MiAaPQ)EBC4107705 035 $a(PPN)193662744 035 $a(EXLCZ)993710000000527103 100 $a20160101h20162016 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to statistical machine learning /$fMasashi Sugiyama 210 1$aAmsterdam :$cElsevier,$d[2016] 210 4$dİ2016 215 $a1 online resource (535 p.) 300 $aDescription based upon print version of record. 311 $a0-12-802121-7 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Introduction to Statistical Machine Learning; Copyright; Table of Contents; Biography; Preface; 1 INTRODUCTION; 1 Statistical Machine Learning; 1.1 Types of Learning; 1.2 Examples of Machine Learning Tasks; 1.2.1 Supervised Learning; 1.2.2 Unsupervised Learning; 1.2.3 Further Topics; 1.3 Structure of This Textbook; 2 STATISTICS AND PROBABILITY; 2 Random Variables and Probability Distributions; 2.1 Mathematical Preliminaries; 2.2 Probability; 2.3 Random Variable and Probability Distribution; 2.4 Properties of Probability Distributions; 2.4.1 Expectation, Median, and Mode 327 $a2.4.2 Variance and Standard Deviation2.4.3 Skewness, Kurtosis, and Moments; 2.5 Transformation of Random Variables; 3 Examples of Discrete Probability Distributions; 3.1 Discrete Uniform Distribution; 3.2 Binomial Distribution; 3.3 Hypergeometric Distribution; 3.4 Poisson Distribution; 3.5 Negative Binomial Distribution; 3.6 Geometric Distribution; 4 Examples of Continuous Probability Distributions; 4.1 Continuous Uniform Distribution; 4.2 Normal Distribution; 4.3 Gamma Distribution, Exponential Distribution, and Chi-Squared Distribution; 4.4 Beta Distribution 327 $a4.5 Cauchy Distribution and Laplace Distribution4.6 t-Distribution and F-Distribution; 5 Multidimensional Probability Distributions; 5.1 Joint Probability Distribution; 5.2 Conditional Probability Distribution; 5.3 Contingency Table; 5.4 Bayes' Theorem; 5.5 Covariance and Correlation; 5.6 Independence; 6 Examples of Multidimensional Probability Distributions; 6.1 Multinomial Distribution; 6.2 Multivariate Normal Distribution; 6.3 Dirichlet Distribution; 6.4 Wishart Distribution; 7 Sum of Independent Random Variables; 7.1 Convolution; 7.2 Reproductive Property; 7.3 Law of Large Numbers 327 $a7.4 Central Limit Theorem8 Probability Inequalities; 8.1 Union Bound; 8.2 Inequalities for Probabilities; 8.2.1 Markov's Inequality and Chernoff's Inequality; 8.2.2 Cantelli's Inequality and Chebyshev's Inequality; 8.3 Inequalities for Expectation; 8.3.1 Jensen's Inequality; 8.3.2 Ho?lder's Inequality and Schwarz's Inequality; 8.3.3 Minkowski's Inequality; 8.3.4 Kantorovich's Inequality; 8.4 Inequalities for the Sum of Independent Random Variables; 8.4.1 Chebyshev's Inequality and Chernoff's Inequality; 8.4.2 Hoeffding's Inequality and Bernstein's Inequality; 8.4.3 Bennett's Inequality 327 $a9 Statistical Estimation9.1 Fundamentals of Statistical Estimation; 9.2 Point Estimation; 9.2.1 Parametric Density Estimation; 9.2.2 Nonparametric Density Estimation; 9.2.3 Regression and Classification; 9.2.4 Model Selection; 9.3 Interval Estimation; 9.3.1 Interval Estimation for Expectation of Normal Samples; 9.3.2 Bootstrap Confidence Interval; 9.3.3 Bayesian Credible Interval; 10 Hypothesis Testing; 10.1 Fundamentals of Hypothesis Testing; 10.2 Test for Expectation of Normal Samples; 10.3 Neyman-Pearson Lemma; 10.4 Test for Contingency Tables 327 $a10.5 Test for Difference in Expectations of Normal Samples 606 $aMachine learning$xStatistical methods 606 $aInformation science$xStatistical methods 606 $aPattern recognition systems 615 0$aMachine learning$xStatistical methods. 615 0$aInformation science$xStatistical methods. 615 0$aPattern recognition systems. 676 $a006.3/1 700 $aSugiyama$b Masashi$f1974-$0847070 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583088403321 996 $aIntroduction to statistical machine learning$91990480 997 $aUNINA