Translator Disclaimer
1 May 2007 Evaluation of two key machine intelligence technologies
Author Affiliations +
This paper summarizes the theory of PLS and K-PLS and support vector machines (SVMs). The advantage of these algorithms is that the regression component can be trained in essentially real time, and these trained algorithms have been shown to provide comparable and consistent results when compared to results obtained by SVMs. A performance trade-off is made between several SVM kernels and K-PLS (as a learning system) using the Gaussian radial basis function kernel. K-PLS and its variants and SVM's provide the theoretical capability to achieve a global minimum, but SVM's are slightly better known. Both approaches can provide very nearly the same non-linear (or linear) decision and regression boundaries, but SVM's are frequently more difficult to train. The hypothesis guiding the research is that K-PLS will provide these boundaries and decision regions with slightly more accuracy and less effort in a real environment, with less computational time. Specifically, for the screen film mammogram data set used, K-PLS resulted in the best performance, with an Az of 0.968. SVM kernels and their respective Az performance results were: 0.870 for the hyperbolic tangent, 0.930 for the s2000, 0.922 for the GRBF, 0.926 for the 2nd order polynomial and dot product kernels. In addition, K-PLS achieved this superior performance result in 53.4% of the time using the GRBF kernel.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Walker H. Land Jr., John Heine, George Tomko, and Robert Thomas "Evaluation of two key machine intelligence technologies", Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600U (1 May 2007);

Back to Top