Performance evaluation of evolutionary computational and conventionally trained support vector machines

Walker H. Land Jr.; John Heine; George Tomko; Alda Mizaku; Swati Gupta; Robert Thomas

doi:10.1117/12.716543

30 April 2007 Performance evaluation of evolutionary computational and conventionally trained support vector machines

Walker H. Land Jr., John Heine, George Tomko, Alda Mizaku, Swati Gupta, Robert Thomas

Proceedings Volume 6560, Intelligent Computing: Theory and Applications V; 65600W (2007) https://doi.org/10.1117/12.716543
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The main objective of this paper is to validate this newly developed Evolutionary Programming (EP) derived Support Vector Machines (SVMs) paradigm by a performance comparison with the accepted conventional iterative gradient method usually used to train these SVMs. The paper first reviews the background research associated with this research problem and follows with the description of the EP developed family of SVMs. Both the mutation and selection methods used to formulate the family of SVMs are described, which is followed by the more familiar Langrangian formulation of SVMs. Kernel based learning methods are then discussed. The concepts described here are not limited to SVMs, and the general principles also apply to other kernel based classifiers as well. Results are depicted for two EP methods: the first a "crude" earlier method described in reference ⁷ and the more recently method described here. Iteratively derived SVM results are also developed for comparison with the EP derived SVM approach. These results show that both methods produced essentially perfect classification AZ results, generally ranging from 0.926 to 0.931. Only the hyperbolic tangent kernel yielded the less accurate result of 0.87. These were expected results because all ambiguous findings were "scrubbed" from the features describing the screen film data set.

Citation Download Citation

Walker H. Land Jr., John Heine, George Tomko, Alda Mizaku, Swati Gupta, and Robert Thomas "Performance evaluation of evolutionary computational and conventionally trained support vector machines", Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600W (30 April 2007); https://doi.org/10.1117/12.716543

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available