Learning from Data Concepts, Theory, and Methods [Hardcover]

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.PREFACE.

NOTATION.

1 Introduction.

1.1 Learning and Statistical Estimation.

1.2 Statistical Dependency and Causality.

1.3 Characterization of Variables.

1.4 Characterization of Uncertainty.

1.5 Predictive Learning versus Other Data Analytical Methodologies.

2 Problem Statement, Classical Approaches, and Adaptive Learning.

2.1 Formulation of the Learning Problem.

2.1.1 Objective of Learning.

2.1.2 Common Learning Tasks.

2.1.3 Scope of the Learning Problem Formulation.

2.2 Classical Approaches.

2.2.1 Density Estimation.

2.2.2 Classification.

2.2.3 Regression.

2.2.4 Solving Problems with Finite Data.

2.2.5 Nonparametric Methods.

2.2.6 Stochastic Approximation.

2.3 Adaptive Learning: Concepts and Inductive Principles.

2.3.1 Philosophy, Major Concepts, and Issues.

2.3.2 A Priori Knowledge and Model Complexity.

2.3.3 Inductive Principles.

2.3.4 Alternative Learning Formulations.<lã…