The Best-Selling Binary Classification Books of All Time

First published in 1983. This monograph is a review of the evolution of George Briggs’ informationprocessing model from a general schema beginning with the work of Saul Sternberg (1969a) and Edward E. Smith (1968) to a fairly well-detailed schematic representation of central processes that Briggs was working on at the time of his early death.

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise.

We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels.

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models.

This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Manyof these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll LearnCompute entropy to detect problematic predictors. Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing. Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling. Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising. Use Monte-Carlo permutation methods to assessthe role of good luck in performance results. Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions. Who This Book is For. Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.