Using Support Vector Machines to Classify Whether a Car is in Front of You or Not

Abstract

Support Vector Machine (SVM) theory is a learning machine theory developed by V. Vapnik. Its most common uses are for classification problems and regression. Like other learning machines, the distribution of the population does not need to be known. It is sufficient only to know that a distribution exists. What sets SVMs apart from other learning machines is its ability to classify items correctly with a relatively small sample size. In this paper I will briefly describe learning machines and SVMs. It will not be a complete tutorial on either subject. If the reader desires to learn more about them, then please refer to the references at the end of this paper. I will also give the theory and results of using SVMs to classify whether there is a car in front of you or not, using an image from a digital camera.

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Document Details

Document Type
Technical Report
Publication Date
Jul 27, 2004
Accession Number
ADA461081

Entities

People

  • Michael S. Del Rose

Organizations

  • Tank-automotive and Armaments Command

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Cameras
  • Classification
  • Digital Cameras
  • Information Operations
  • Learning
  • Learning Machines
  • Supervised Machine Learning

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks