Pattern Recognition and Machine Learning
نویسندگان
چکیده
In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the Support Vector Machine” section (the section “Lagrange Multipliers and Lagrangian Duality” is not required). The SMO algorithm is due to Platt [2], but we make use of algorithmic improvements proposed by Keerthi et.al. [1]. The interested reader is encouraged to study these references as well. The notation we employ here is the same as in the SVM chapter of the course notes. SMO solves the dual of the soft margin SVM problem:
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