The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM

Abstract

Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of the C-SVM and the nu-SVM, which we denote the 2C-SVM and 2nu-SVM respectively. The C-SVM and the nu-SVM are known to be closely related, and we prove that the 2C-SVM and 2nu-SVM share a similar relationship. This demonstrates that the 2C-SVM and 2nu-SVM explore the same space of possible classifiers, and gives us a clear understanding of the parameter space for both versions.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2005
Accession Number
ADA487814

Entities

People

  • Mark A. Davenport

Organizations

  • Rice University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Analogs
  • Boundaries
  • Classification
  • Computers
  • Engineering
  • Hilbert Space
  • Information Operations
  • Machine Learning
  • Probability
  • Standards
  • Supervised Machine Learning
  • Training
  • Universities

Fields of Study

  • Computer science

Readers

  • Marine Ecological Systems Migration
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space