Progressive refinement for support vector machines

Authors:
Kiri L. Wagstaff;Michael Kocurek;Dominic Mazzoni;Benyang Tang
Affiliations:
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA 91109;California Institute of Technology, Pasadena, USA 91125;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA 91109 and Google Inc., Santa Monica, USA;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA 91109
Venue:
Data Mining and Knowledge Discovery
Year:
2010

Citing 7
Cited 0

Support-Vector Networks

Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Training Invariant Support Vector Machines

Machine Learning
Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiclass reduced-set support vector machines

ICML '06 Proceedings of the 23rd international conference on Machine learning
On-board analysis of uncalibrated data for a spacecraft at mars

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Which is the best multiclass SVM method? an empirical study

MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support vector machines (SVMs) have good accuracy and generalization properties, but they tend to be slow to classify new examples. In contrast to previous work that aims to reduce the time required to fully classify all examples, we present a method that provides the best-possible classification given a specific amount of computational time. We construct two SVMs: a "full" SVM that is optimized for high accuracy, and an approximation SVM (via reduced-set or subset methods) that provides extremely fast, but less accurate, classifications. We apply the approximate SVM to the full data set, estimate the posterior probability that each classification is correct, and then use the full SVM to reclassify items in order of their likelihood of misclassification. Our experimental results show that this method rapidly achieves high accuracy, by selectively devoting resources (reclassification) only where needed. It also provides the first such progressive SVM solution that can be applied to multiclass problems.