[PDF] Top 20 Linear Convergence of a Decomposition Method for Support Vector Machines

... asymptotic convergence of some commonly used decomposition methods for support vector machines has been ...local convergence rates are still ...the linear ... See full document

... Its convergence properties have not been fully ...asymptotic convergence was first proposed by Chang et ...implementation. A later breakthrough by Keerthi and Gilbert proved the convergence ... See full document

... both decomposition methods take many more iterations. For most problems we have tested, not only those presented here, we observe slow convergence of decomposition methods when C is ... See full document

... “decomposition method.” However, there is no convergence proof so ...this method to projected gradient methods and provide theoretical proofs for a version of ... See full document

... Analysis of Stopping Criteria of Decomposition Methods for Support Vector Machines Chih-Jen Lin, Member, IEEE Abstract—In a previous paper, we proved the ... See full document

... 2. Of course the above shrinking strategy may be too ...the decomposition method has a very slow convergence and a large portion of iterations are spent for ... See full document

... implementation for RSVM which solves a linear equation for each parameter set, the training time of all other approaches depends on ...number of iterations may vary for ... See full document

... 5.4. For four RSVM implementations, we find their testing time is quite close so we post only that of the decomposition ...time for solving the optimal model. Except the LS-SVM implementation ... See full document

... Lin. Linear convergence of a decomposition method for support vector ...Department of Computer Science, National Taiwan University, ... See full document

... (Due to space limit, we discuss the difference of their implementations in [Z].) Thus, it is very difficult to conduct a fair comparison. Our goal here is only to demonstrate.. tha[r] ... See full document

... Drawbacks of Decomposition Methods for Lin- ear SVMs with n ¿ l The decomposition method is an iterative ...set of variables is separated to two sets B and N, where B is the ... See full document

... Abstract Linear support vector machines (SVM) are useful for classifying large-scale sparse ...propose a novel coordinate descent algorithm for training linear SVM ... See full document

... at a bound, it is inactivated. Thus the set A of activated variables is dynamically reduced in every min(l, 1000) ...2. Of course the above shrinking strategy may be too ...position ... See full document

... learning method proceeds by generating vector sequences of iterates, residuals corresponding to iterates, and search directions used in updating iterates and ...iteration of the method, ... See full document

... Technical report, Department of Computer Science and Information Engineering, National Taiwan University, 2000. On the convergence of the decomposi- tion method for support vec[r] ... See full document

... choice for solving ...better convergence properties. 2.2. Our Approach: Newton’s Method with Backtracking As indicated by Platt (2000), any method for unconstrained optimiza- tion can ... See full document

... Implementations of RBF networks and SVM are also ...becomes a linear system, sometimes a direct method such as Gaussian elimination is ...considered. For the modified form ... See full document

... Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular ... See full document

... Newton’s method. The advantage of using the second- order information is to have fast convergence for a high ...that a high precision is not important on improving the test ... See full document

... • Engine misfire: a substantial fraction of a cylinder’s air-fuel mixture fails to ignite.. • Frequent misfires: pollutants and costly replacement[r] ... See full document