Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weig...

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Bibliographic Details
Main Authors: Schoelkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.
Language:en_US
Published: 2004
Subjects:
AI
MIT
Artificial Intelligence
radial basis function networks
support vector machines
pattern recognition
machine learning
VC-dimension
performance comparison
model selection
Online Access:http://hdl.handle.net/1721.1/7180

Internet

http://hdl.handle.net/1721.1/7180

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