Title :
Rigorous proof of termination of SMO algorithm for support vector Machines
Author :
Takahashi, Naoyuki ; Nishi, Tomoki
Author_Institution :
Dept. of Comput. Sci. & Commun. Eng., Kyushu Univ., Fukuoka, Japan
fDate :
5/1/2005 12:00:00 AM
Abstract :
Sequential minimal optimization (SMO) algorithm is one of the simplest decomposition methods for learning of support vector machines (SVMs). Keerthi and Gilbert have recently studied the convergence property of SMO algorithm and given a proof that SMO algorithm always stops within a finite number of iterations. In this letter, we point out the incompleteness of their proof and give a more rigorous proof.
Keywords :
convergence; optimisation; support vector machines; convergence; rigorous proof; sequential minimal optimization; support vector machine; Algorithm design and analysis; Convergence; Machine learning; Machine learning algorithms; Matrix decomposition; Neural networks; Optimization methods; Pattern recognition; Quadratic programming; Support vector machines; Support vector machines (SVMs); convergence; sequential minimal optimization (SMO) algorithm; termination; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Computing Methodologies; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.844857
