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Related Experiment Video

Updated: May 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Efficient classification for additive kernel SVMs.

Subhransu Maji1, Alexander C Berg, Jitendra Malik

  • 1Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL 60637, USA. smaji@ttic.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 7, 2012
PubMed
Summary
This summary is machine-generated.

We developed efficient nonlinear kernel Support Vector Machines (SVMs) using additive kernels. This method offers improved accuracy and practical runtime for large-scale image recognition and real-time detection tasks.

Related Experiment Videos

Last Updated: May 24, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Computer Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Support Vector Machines (SVMs) are powerful classification tools.
  • Evaluating nonlinear kernel SVMs can be computationally expensive due to the number of support vectors.
  • Efficient kernel methods are crucial for large-scale machine learning tasks.

Purpose of the Study:

  • To develop an efficient method for evaluating nonlinear kernel SVMs.
  • To introduce a class of additive kernels that allow for complexity independent of support vectors.
  • To demonstrate practical improvements in accuracy and runtime for image recognition tasks.

Main Methods:

  • Introduced additive kernels, a class of nonlinear kernels including intersection and chi-squared kernels.
  • Developed approximate classifiers with runtime and memory complexity independent of support vector count.
  • Conducted experiments on diverse datasets (e.g., INRIA person, Caltech-101, MNIST).

Main Results:

  • Additive kernel SVMs achieve accuracy comparable to or better than linear SVMs.
  • The proposed method offers significant runtime and memory efficiency.
  • Demonstrated effectiveness on various image datasets and real-world applications.

Conclusions:

  • Additive kernel SVMs provide a practical and efficient solution for large-scale recognition and real-time detection.
  • The method enhances the applicability of SVMs in various machine learning domains.
  • The techniques are adaptable for weighted additive kernels and accelerate SVM training.