Machine Learning, Neural and Statistical Classification
by D. Michie, D. J. Spiegelhalter
Publisher: Ellis Horwood 1994
Number of pages: 298
The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. As the book's title suggests. a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified: statistical, machine learning and neural network.
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by Aaron Hertzmann - University of Toronto
Contents: Introduction to Machine Learning; Linear Regression; Nonlinear Regression; Quadratics; Basic Probability Theory; Probability Density Functions; Estimation; Classification; Gradient Descent; Cross Validation; Bayesian Methods; and more.
Contents: Introduction and Main Principles; Background and Preliminaries; Knowledge discovery in Databases; Reasoning; Search Methods; Statistics; Main Learning Paradigms; Classification Tasks; Online Learning; Semi-supervised learning; etc.
by Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
by Stephen Muggleton, Luc de Raedt - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.