Stochastic Optimization for Large-scale Machine Learning

图书简介

Advancements in the technology and availability of data sources have led to the `Big Data’ era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.Key Features:

Bridges machine learning and Optimisation.

Bridges theory and practice in machine learning.

Identifies key research areas and recent research directions to solve large-scale machine learning problems.

Develops optimisation techniques to improve machine learning algorithms for big data problems.The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

List of Figures List of Tables Preface Section I BACKGROUND Introduction 1.1 LARGE-SCALE MACHINE LEARNING 1.2 OPTIMIZATION PROBLEMS 1.3 LINEAR CLASSIFICATION 1.3.1 Support Vector Machine (SVM) 1.3.2 Logistic Regression 1.3.3 First and Second Order Methods 1.3.3.1 First Order Methods 1.3.3.2 Second Order Methods 1.4 STOCHASTIC APPROXIMATION APPROACH 1.5 COORDINATE DESCENT APPROACH 1.6 DATASETS 1.7 ORGANIZATION OF BOOK Optimisation Problem, Solvers, Challenges and Research Directions 2.1 INTRODUCTION 2.1.1 Contributions 2.2 LITERATURE 2.3 PROBLEM FORMULATIONS 2.3.1 Hard Margin SVM (1992) 2.3.2 Soft Margin SVM (1995) 2.3.3 One-versus-Rest (1998) 2.3.4 One-versus-One (1999) 2.3.5 Least Squares SVM (1999) 2.3.6 v-SVM (2000) 2.3.7 Smooth SVM (2001) 2.3.8 Proximal SVM (2001) 2.3.9 Crammer Singer SVM (2002) 2.3.10 Ev-SVM (2003) 2.3.11 Twin SVM (2007) 2.3.12 Capped lp-norm SVM (2017) 2.4 PROBLEM SOLVERS 2.4.1 Exact Line Search Method 2.4.2 Backtracking Line Search 2.4.3 Constant Step Size 2.4.4 Lipschitz

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Stochastic Optimization for Large-scale Machine Learning 大规模机器学习的随机优化 工业工程学

Stochastic Optimization for Large-scale Machine Learning

大规模机器学习的随机优化
工业工程学