Title :
An array processor architecture for support vector learning
Author :
To, K. ; Lim, C.C. ; Beaumont-Smith, A. ; Liebelt, M.J. ; Marwood, W.
Author_Institution :
Dept. of Electr. & Electron. Eng., Adelaide Univ., SA, Australia
Abstract :
Support vector training requires the evaluation of a quadratic programming (QP) problem which is computationally intensive. In addition, the size of the QP is dependent on the number of training samples and may exceed the memory size. This paper presents a fast parallel implementation of the SVM on an array processor which is optimised for matrix operations. A decomposition algorithm is used to break large scale support vector problems into a fixed size block for efficient processing in the array
Keywords :
learning (artificial intelligence); parallel architectures; quadratic programming; array processor architecture; decomposition algorithm; fast parallel implementation; fixed size block; large scale support vector problems; matrix operations; quadratic programming problem; support vector learning; support vector training; Computer architecture; Constraint optimization; Large-scale systems; Learning systems; Matrix decomposition; Parallel processing; Quadratic programming; Scalability; Support vector machine classification; Support vector machines;
Conference_Titel :
Knowledge-Based Intelligent Information Engineering Systems, 1999. Third International Conference
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-5578-4
DOI :
10.1109/KES.1999.820202
