Year: 2005
Type: article
Abstract: Margin-based classifiers defined by functional margins are generally believed to yield high performance in classification.In this thesis, a general theory that quantifies the size of generalization error of a margin classifier is presented.The tradeoff between geometric margins and training errors is captured, in addition to the complexity of a classification problem.The theory permits an investigation of the generalization ability of convex and nonconvex margin classifiers, including support vector machines (SVM), kernel logistic regression (KLR), and ψ-learning.Our theory indicates that the generalization ability of a certain class of nonconvex losses may be substantially faster than those for convex losses.Illustrative examples for both linear and nonlinear classification are provided.
Author: Changyi Park
Institution: The Ohio State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2598086901