Kernel Based Algorithms for Mining Huge Data Sets Supervised, Semi-supervised, and Unsupervised Learning /

• Main Authors: Huang, Te-Ming (Author), Kecman, Vojislav (Author), Kopriva, Ivica (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Series: Studies in Computational Intelligence, 17
• Subjects: Computer science.; Data mining.; Artificial intelligence.; Applied mathematics.; Engineering mathematics.; Computer Science.; Data Mining and Knowledge Discovery.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in...

Table of Contents:

• Support Vector Machines in Classification and Regression — An Introduction
• Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance
• Feature Reduction with Support Vector Machines and Application in DNA Microarray Analysis
• Semi-supervised Learning and Applications
• Unsupervised Learning by Principal and Independent Component Analysis.