Introduction to Predictive Learning

The subject of data-driven modeling has been addressed in various disciplines such as statistics, pattern recognition, signal processing, genomics, artificial neural networks, machine learning, and data mining, which adopt specialized terminology and conceptual frameworks to motivate various learning algorithms, in spite of the close similarity (equivalence) between actual algorithms. The main commonality between these methodologies is that they all develop algorithms for estimating predictive models from data, albeit providing quite different motivation for these algorithms.This textbook, accessible to undergraduate students and practitioners, emphasizes the methodology and principles of predictive learning, rather than specialized terminology or detailed description of learning algorithms. Introduction to Predictive Learning adopts the conceptual framework developed in Vapnik-Chervonenkis (VC) theory, focusing on the methodological and practical aspects of VC-theory rather than its technical details.