Support Vector Machines: Theory and Applications

V. Kecman (auth.), Professor Lipo Wang (eds.)9783540243885, 3-540-24388-7

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in the respective fields.

Table of contents :
Support Vector Machines – An Introduction….Pages 1-47
Multiple Model Estimation for Nonlinear Classification….Pages 49-76
Componentwise Least Squares Support Vector Machines….Pages 77-98
Active Support Vector Learning with Statistical Queries….Pages 99-111
Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine….Pages 113-131
Active-Set Methods for Support Vector Machines….Pages 133-158
Theoretical and Practical Model Selection Methods for Support Vector Classifiers….Pages 159-179
Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification….Pages 181-203
Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods….Pages 205-218
An Accelerated Robust Support Vector Machine Algorithm….Pages 219-232
Fuzzy Support Vector Machines with Automatic Membership Setting….Pages 233-254
Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance….Pages 255-274
Kernel Discriminant Learning with Application to Face Recognition….Pages 275-296
Fast Color Texture-Based Object Detection in Images: Application to License Plate Localization….Pages 297-320
Support Vector Machines for Signal Processing….Pages 321-342
Cancer Diagnosis and Protein Secondary Structure Prediction Using Support Vector Machines….Pages 343-363
Gas Sensing Using Support Vector Machines….Pages 365-386
Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing….Pages 387-397
Application of Support Vector Machine to the Detection of Delayed Gastric Emptying from Electrogastrograms….Pages 399-412
Tachycardia Discrimination in Implantable Cardioverter Defibrillators Using Support Vector Machines and Bootstrap Resampling….Pages 413-431