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Machine Learning and SVM

Tony UK "dao719"
 
MACHINE LEARNING (Int'l Ed) (Mcgraw-Hill International Edit)
MACHINE LEARNING (Int'l Ed) (Mcgraw-Hill International Edit)
"Excellent introduction covering many techniques including neural nets and decision tree learning, along with some theoretical discussion."
Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems)
"Good introduction to data mining, including machine learning techniques. Also provides useful introduction to the WEKA development system."
An Introduction to Kolmogorov Complexity and Its Applications (Texts in Computer Science)
An Introduction to Kolmogorov Complexity and Its Applications (Texts in Computer Science)
"Kolmogorov complexity provides a theoretical foundation for induction and machine learning. This is the classic introduction. Quite heavy but worth persevering."
Pattern Classification, Second Edition: 1 (A Wiley-Interscience publication)
Pattern Classification, Second Edition: 1 (A Wiley-Interscience publication)
"An encyclopedic overview of the major classification algorithms including clustering techniques."
Statistical Learning Theory (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning,       Communications and Control)
Statistical Learning Theory (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)
"Foundations of statistical learning theory and support vector machines."
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
"Excellent introduction to support vector machines, going into great detail and including the theoretical foundation."
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
"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."
Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)
Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)
"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."