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This document presents a generalization of the Sequential Minimal Optimization (SMO) algorithm for training Support Vector Machines (SVMs) to handle regression problems, addressing the challenges of slow convergence rates in non-sparse data sets. The authors introduce modifications that allow for effective caching of kernel function outputs, resulting in significant improvements in convergence speed for regression tasks. The paper details the theoretical framework, modifications made to SMO, and provides nu

Author

Raenald syaputra

Language

EN