Instance-Based penalization techniques for classification
2007-01-06
Research Paper
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Several instance-based large-margin classi¯ers have recently been put forward in the literature: Support Hyperplanes, Nearest Convex Hull classifier, and Soft Nearest Neighbor. We examine those techniques from a common fit-versus-complexity framework and study the links be- tween them. Finally, we compare the performance of these techniques vis-a-vis each other and other standard classification methods.
Automatically Extracted Terms
- hyperplane
- classification
- training data
- point
- training
- distance
- technique
- label
- error
- class
- observation
- test point x
- support vector machines
- method
- data sets
- problem
- machine
- support
- penalization
- optimization
