Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).
Necessary Mathematical Concepts; Support Vector Machines (SVMs) for Binary Classification: Classical Formulation; Basic Principles of Statistical Machine Learning; Model Selection for SVMs; SVMs for Multi-Category Classification; Support Vector Regression (SVR); Novelty Detection with SVM-Based Methods; Support Vector Clustering; SVM-Based Variable Selection; Computing Posterior Class Probabilities For SVM Classifiers.