Machine Learning and SVM

The list author says:
	"Excellent introduction covering many techniques including neural nets and decision tree learning, along with some theoretical discussion."

The list author says:
	"Good introduction to data mining, including machine learning techniques. Also provides useful introduction to the WEKA development system."

The list author says:
	"Kolmogorov complexity provides a theoretical foundation for induction and machine learning. This is the classic introduction. Quite heavy but worth persevering."

The list author says:
	"An encyclopedic overview of the major classification algorithms including clustering techniques."

The list author says:
	"Foundations of statistical learning theory and support vector machines."

The list author says:
	"Excellent introduction to support vector machines, going into great detail and including the theoretical foundation."

The list author says:
	"Deals with the often overlooked issue of calibrating the reliability of learning algorithms. This book provides a framework for algorithms that control risk of error through hedged predictions."

The list author says:
	"Well-written account of using the kernel method in machine learning and learning theory. Several kernelized algorithms are discussed including SVM, discriminant analysis and PCA."