| Description |
1 online resource |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Online resource; title from PDF title page (ScienceDirect, viewed February 16, 2016). |
| Contents |
Front 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 -- 2.4.2 Variance and Standard Deviation -- 2.4.3 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 -- 4.5 Cauchy Distribution and Laplace Distribution -- 4.6 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. |
|
7.4 Central Limit Theorem -- 8 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 Hölder'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 -- 9 Statistical Estimation -- 9.1 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 -- 10.5 Test for Difference in Expectations of Normal Samples -- 10.5.1 Two Samples without Correspondence -- 10.5.2 Two Samples with Correspondence -- 10.6 Nonparametric Test for Ranks -- 10.6.1 Two Samples without Correspondence -- 10.6.2 Two Samples with Correspondence -- 10.7 Monte Carlo Test -- 3 GENERATIVE APPROACH TO STATISTICAL PATTERN RECOGNITION -- 11 Pattern Recognition via Generative Model Estimation -- 11.1 Formulation of Pattern Recognition -- 11.2 Statistical Pattern Recognition -- 11.3 Criteria for Classifier Training -- 11.3.1 MAP Rule -- 11.3.2 Minimum Misclassification Rate Rule -- 11.3.3 Bayes Decision Rule -- 11.3.4 Discussion. |
|
11.4 Generative and Discriminative Approaches -- 12 Maximum Likelihood Estimation -- 12.1 Definition -- 12.2 Gaussian Model -- 12.3 Computing the Class-Posterior Probability -- 12.4 Fisher's Linear Discriminant Analysis (FDA) -- 12.5 Hand-Written Digit Recognition -- 12.5.1 Preparation -- 12.5.2 Implementing Linear Discriminant Analysis -- 12.5.3 Multiclass Classification -- 13 Properties of Maximum Likelihood Estimation -- 13.1 Consistency -- 13.2 Asymptotic Unbiasedness -- 13.3 Asymptotic Efficiency -- 13.3.1 One-Dimensional Case -- 13.3.2 Multidimensional Cases -- 13.4 Asymptotic Normality -- 13.5 Summary -- 14 Model Selection for Maximum Likelihood Estimation -- 14.1 Model Selection -- 14.2 KL Divergence -- 14.3 AIC -- 14.4 Cross Validation -- 14.5 Discussion -- 15 Maximum Likelihood Estimation for Gaussian Mixture Model -- 15.1 Gaussian Mixture Model -- 15.2 MLE -- 15.3 Gradient Ascent Algorithm -- 15.4 EM Algorithm -- 16 Nonparametric Estimation -- 16.1 Histogram Method -- 16.2 Problem Formulation -- 16.3 KDE -- 16.3.1 Parzen Window Method -- 16.3.2 Smoothing with Kernels -- 16.3.3 Bandwidth Selection -- 16.4 NNDE -- 16.4.1 Nearest Neighbor Distance -- 16.4.2 Nearest Neighbor Classifier -- 17 Bayesian Inference -- 17.1 Bayesian Predictive Distribution -- 17.1.1 Definition -- 17.1.2 Comparison with MLE -- 17.1.3 Computational Issues -- 17.2 Conjugate Prior -- 17.3 MAP Estimation -- 17.4 Bayesian Model Selection -- 18 Analytic Approximation of Marginal Likelihood -- 18.1 Laplace Approximation -- 18.1.1 Approximation with Gaussian Density -- 18.1.2 Illustration -- 18.1.3 Application to Marginal Likelihood Approximation -- 18.1.4 Bayesian Information Criterion (BIC) -- 18.2 Variational Approximation -- 18.2.1 Variational Bayesian EM (VBEM) Algorithm -- 18.2.2 Relation to Ordinary EM Algorithm -- 19 Numerical Approximation of Predictive Distribution. |
|
19.1 Monte Carlo Integration -- 19.2 Importance Sampling -- 19.3 Sampling Algorithms -- 19.3.1 Inverse Transform Sampling -- 19.3.2 Rejection Sampling -- 19.3.3 Markov Chain Monte Carlo (MCMC) Method -- 20 Bayesian Mixture Models -- 20.1 Gaussian Mixture Models -- 20.1.1 Bayesian Formulation -- 20.1.2 Variational Inference -- 20.1.3 Gibbs Sampling -- 20.2 Latent Dirichlet Allocation (LDA) -- 20.2.1 Topic Models -- 20.2.2 Bayesian Formulation -- 20.2.3 Gibbs Sampling -- 4 DISCRIMINATIVE APPROACH TO STATISTICAL MACHINE LEARNING -- 21 Learning Models -- 21.1 Linear-in-Parameter Model -- 21.2 Kernel Model -- 21.3 Hierarchical Model -- 22 Least Squares Regression -- 22.1 Method of LS -- 22.2 Solution for Linear-in-Parameter Model -- 22.3 Properties of LS Solution -- 22.4 Learning Algorithm for Large-Scale Data -- 22.5 Learning Algorithm for Hierarchical Model -- 23 Constrained LS Regression -- 23.1 Subspace-Constrained LS -- 23.2?2-Constrained LS -- 23.3 Model Selection -- 24 Sparse Regression -- 24.1?1-Constrained LS -- 24.2 Solving?1-Constrained LS -- 24.3 Feature Selection by Sparse Learning -- 24.4 Various Extensions -- 24.4.1 Generalized?1-Constrained LS -- 24.4.2?p-Constrained LS -- 24.4.3?1+?2-Constrained LS -- 24.4.4?1,2-Constrained LS -- 24.4.5 Trace Norm Constrained LS -- 25 Robust Regression -- 25.1 Nonrobustness of?2-Loss Minimization -- 25.2?1-Loss Minimization -- 25.3 Huber Loss Minimization -- 25.3.1 Definition -- 25.3.2 Stochastic Gradient Algorithm -- 25.3.3 Iteratively Reweighted LS -- 25.3.4?1-Constrained Huber Loss Minimization -- 25.4 Tukey Loss Minimization -- 26 Least Squares Classification -- 26.1 Classification by LS Regression -- 26.2 0/1-Loss and Margin -- 26.3 Multiclass Classification -- 27 Support Vector Classification -- 27.1 Maximum Margin Classification -- 27.1.1 Hard Margin Support Vector Classification. |
|
27.1.2 Soft Margin Support Vector Classification -- 27.2 Dual Optimization of Support Vector Classification -- 27.3 Sparseness of Dual Solution -- 27.4 Nonlinearization by Kernel Trick -- 27.5 Multiclass Extension -- 27.6 Loss Minimization View -- 27.6.1 Hinge Loss Minimization -- 27.6.2 Squared Hinge Loss Minimization -- 27.6.3 Ramp Loss Minimization -- 28 Probabilistic Classification -- 28.1 Logistic Regression -- 28.1.1 Logistic Model and MLE -- 28.1.2 Loss Minimization View -- 28.2 LS Probabilistic Classification -- 29 Structured Classification -- 29.1 Sequence Classification -- 29.2 Probabilistic Classification for Sequences -- 29.2.1 Conditional Random Field -- 29.2.2 MLE -- 29.2.3 Recursive Computation -- 29.2.4 Prediction for New Sample -- 29.3 Deterministic Classification for Sequences -- 5 FURTHER TOPICS -- 30 Ensemble Learning -- 30.1 Decision Stump Classifier -- 30.2 Bagging -- 30.3 Boosting -- 30.3.1 Adaboost -- 30.3.2 Loss Minimization View -- 30.4 General Ensemble Learning -- 31 Online Learning -- 31.1 Stochastic Gradient Descent -- 31.2 Passive-Aggressive Learning -- 31.2.1 Classification -- 31.2.2 Regression -- 31.3 Adaptive Regularization of Weight Vectors (AROW) -- 31.3.1 Uncertainty of Parameters -- 31.3.2 Classification -- 31.3.3 Regression -- 32 Confidence of Prediction -- 32.1 Predictive Variance for?2-Regularized LS -- 32.2 Bootstrap Confidence Estimation -- 32.3 Applications -- 32.3.1 Time-series Prediction -- 32.3.2 Tuning Parameter Optimization -- 33 Semisupervised Learning -- 33.1 Manifold Regularization -- 33.1.1 Manifold Structure Brought by Input Samples -- 33.1.2 Computing the Solution -- 33.2 Covariate Shift Adaptation -- 33.2.1 Importance Weighted Learning -- 33.2.2 Relative Importance Weighted Learning -- 33.2.3 Importance Weighted Cross Validation -- 33.2.4 Importance Estimation. |
| Summary |
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. |
| Subject |
Machine learning -- Statistical methods.
|
|
Apprentissage automatique -- Méthodes statistiques.
|
|
COMPUTERS -- General.
|
|
Machine learning -- Statistical methods
|
| Other Form: |
Print version: 9780128023501 |
| ISBN |
9780128023501 (electronic bk.) |
|
0128023503 (electronic bk.) |
|
9780128021217 |
|
0128021217 (pbk.) |
|
9780128021217 (pbk.) |
| Standard No. |
AU@ 000056966357 |
|
CHNEW 001013278 |
|
CHVBK 519309782 |
|
DEBBG BV043968390 |
|
DEBSZ 478823118 |
|
DEBSZ 480345333 |
|
DEBSZ 485788896 |
|
GBVCP 845241885 |
|
GBVCP 879389826 |
|
GBVCP 882749455 |
|
