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A New Framework for Machine Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationComputational Intelligence: Research Frontiers
Subtitle of host publicationIEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China, June 1-6, 2008, Plenary/Invited Lectures
EditorsJacekM. Zurada, GaryG. Yen, Jun Wang
PublisherSpringer Berlin Heidelberg
Pages1-24
Number of pages24
ISBN (Electronic)978-3-540-68860-0
ISBN (Print)978-3-540-68858-7
DOIs
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume5050
ISSN (Print)0302-9743

Abstract

The last five years have seen the emergence of a powerful new framework for building sophisticated real-world applications based on machine learning. The cornerstones of this approach are (i) the adoption of a Bayesian viewpoint, (ii) the use of graphical models to represent complex probability distributions, and (iii) the development of fast, deterministic inference algorithms, such as variational Bayes and expectation propagation, which provide efficient solutions to inference and learning problems in terms of local message passing algorithms. This paper reviews the key ideas behind this new framework, and highlights some of its major benefits. The framework is illustrated using an example large-scale application.

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