Abstract:
This paper extends the well-known SMO (Sequential Minimal Optimization) algorithm of Support Vector Machines (SVMs) to Least Squares SVM formulation. The algorithm is asy...Show MoreMetadata
Abstract:
This paper extends the well-known SMO (Sequential Minimal Optimization) algorithm of Support Vector Machines (SVMs) to Least Squares SVM formulation. The algorithm is asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.
Date of Conference: 20-24 July 2003
Date Added to IEEE Xplore: 26 August 2003
Print ISBN:0-7803-7898-9
Print ISSN: 1098-7576
Contents 