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Stochastic Coordinate Descent (SCD) methods are among the first optimization schemes suggested for efficiently solving large scale problems. However, until now, there exists a gap between the convergence rate analysis and practical SCD algorithms for general smooth losses and there is no primal SCD algorithm for nonsmooth losses. In this paper, we discuss(More)
In this paper, the basic concepts and survey of the available literature on Support Vector Machines (SVM) in data mining and soft computing research area is provided. While at the time of survey several new methods were found related to SVM like as Support Vector Representation and Discrimination Machine (SVRDM), Recursive SVM (RSVM), On-line Independent(More)
In this paper, a chaotic model is designed and implemented based on Logistic map, then expansed control parameters, thus a double chaotic orbits are produced, they can against reconstruction of threats chaos. and disturbance of the method using Fibonacci sequence is mapped to an integer space, resolve the finite precision issue in the computer application,(More)
The truncated regular L₁-loss support vector machine can eliminate the excessive number of support vectors (SVs); thus, it has significant advantages in robustness and scalability. However, in this paper, we discover that the associated state-of-the-art solvers, such as difference convex algorithm and concave-convex procedure, not only have limited sparsity(More)
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