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Revised algorithm for online learning with kernels (OLK) in classification and regression is proposed in a reproducing kernel hilbert space (RKHS). Compared with the original OLK, the revised algorithm allows that the new data points arrive either one by one or two by two.
New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models...
New optimization models and algorithms for online learning with kernels (OLK) in regression are proposed in a Reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “forgetting” factor in the model makes it possible that the memory requirement of the algorithm can be bounded as the learning process continues. The applications of the proposed OLK algorithms in classification...
New optimization models and algorithms for online learning with kernels (OLK) in classification and regression are proposed in a Reproducing Kernel Hilbert Space (RKHS) by solving a constrained optimization model. The “forgetting” factor in the model makes it possible that the memory requirement of the algorithm can be bounded as the learning process continues. The applications of the proposed OLK...
A new optimization problem together with its model for online learning with kernels in novelty detection is formulated in a Reproducing Kernel Hilbert Space (RKHS). By exploiting the techniques of Lagrange dual problem in a similar way to Vapnik's support vector machine (SVM), the optimization problem is solved iteratively and this gives an algorithm named online learning with kernels denoted as (OLK...
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