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1. |
Record Nr. |
UNISA996466521103316 |
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Autore |
Baum Paul Frank |
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Titolo |
Topics in Algebraic and Topological K-Theory [[electronic resource] /] / by Paul Frank Baum, Guillermo Cortiñas, Ralf Meyer, Rubén Sánchez-García, Marco Schlichting, Bertrand Toën ; edited by Guillermo Cortiñas |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 |
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ISBN |
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Edizione |
[1st ed. 2011.] |
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Descrizione fisica |
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1 online resource (XVI, 308 p. 2 illus.) |
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Collana |
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Lecture Notes in Mathematics, , 0075-8434 ; ; 2008 |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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K-theory for group C*-algebras -- Universal Coefficient Theorems and assembly maps in KK-theory -- Algebraic v. topological K-theory: a friendly match -- Higher algebraic K-theory (after Quillen, Thomason and others) -- Lectures on DG-categories. |
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Sommario/riassunto |
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This volume is an introductory textbook to K-theory, both algebraic and topological, and to various current research topics within the field, including Kasparov's bivariant K-theory, the Baum-Connes conjecture, the comparison between algebraic and topological K-theory of topological algebras, the K-theory of schemes, and the theory of dg-categories. |
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2. |
Record Nr. |
UNINA9910583088403321 |
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Autore |
Sugiyama Masashi <1974-> |
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Titolo |
Introduction to statistical machine learning / / Masashi Sugiyama |
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Pubbl/distr/stampa |
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Amsterdam : , : Elsevier, , [2016] |
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©2016 |
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ISBN |
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Descrizione fisica |
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1 online resource (535 p.) |
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Disciplina |
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Soggetti |
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Machine learning - Statistical methods |
Information science - Statistical methods |
Pattern recognition systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 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 |
4.5 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 |
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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 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 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 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 |
10.5 Test for Difference in Expectations of Normal Samples |
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