Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149607 | Journal of Statistical Planning and Inference | 2009 | 9 Pages |
Abstract
This paper develops a general approach to quantifying the size of generalization errors for margin-based classification. A trade-off between geometric margins and training errors is exhibited along with the complexity of a binary classification problem. Consequently, this results in dealing with learning theory in a broader framework, in particular, of handling both convex and non-convex margin classifiers, among which includes, support vector machines, kernel logistic regression, and ψψ-learning. Examples for both linear and nonlinear classifications are provided.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Changyi Park,