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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

The MIT Press (2001)

A comprehensive introduction to Support Vector Machines and related kernel methods.

Machine Learning for Data Streams: with Practical Examples in MOA

Machine Learning for Data Streams

with Practical Examples in MOA

Albert Bifet, Ricard Gavaldà and 2 more...
The MIT Press (2018)

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with...

Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
The MIT Press (2012)

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift”...

Learning Kernel Classifiers: Theory and Algorithms
The MIT Press (2001)

An overview of the theory and application of kernel classification methods.

The Minimum Description Length Principle
The MIT Press (2007)

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle...

Gaussian Processes for Machine Learning
The MIT Press (2005)

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled,...

Semi-Supervised Learning
The MIT Press (2010)

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in...

Introduction to Statistical Relational Learning
The MIT Press (2007)

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of...

Graphical Models for Machine Learning and Digital Communication
The MIT Press (1998)

A variety of problems in machine learning and digital communication deal with complex but structured...