翻訳と辞書
Words near each other
・ Sequential Circuits Studio 440
・ Sequential consistency
・ Sequential coupling
・ Sequential decoding
・ Sequential dynamical system
・ Sequential equilibrium
・ Sequential estimation
・ Sequential euclidean distance transforms
・ Sequential function chart
・ Sequential game
・ Sequential hermaphroditism
・ Sequential high-dose chemotherapy
・ Sequential lineups
・ Sequential logic
・ Sequential manual transmission
Sequential minimal optimization
・ Sequential model
・ Sequential pattern mining
・ Sequential probability ratio test
・ Sequential proportional approval voting
・ Sequential quadratic programming
・ Sequential receivers
・ Sequential space
・ Sequential structure alignment program
・ Sequential system
・ Sequential time
・ Sequential transmission
・ Sequential walking
・ Sequentially compact space
・ Sequenza


Dictionary Lists
翻訳と辞書 辞書検索 [ 開発暫定版 ]
スポンサード リンク

Sequential minimal optimization : ウィキペディア英語版
Sequential minimal optimization

Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support vector machines. It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.〔Luca Zanni (2006). ''(Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems )''.〕 The publication of the SMO algorithm in 1998 has generated a lot of excitement in the SVM community, as previously available methods for SVM training were much more complex and required expensive third-party QP solvers.
== Optimization problem ==
(詳細はbinary classification problem with a dataset (''x''1, ''y''1), ..., (''x''''n'', ''y''''n''), where ''x''''i'' is an input vector and is a binary label corresponding to it. A soft-margin support vector machine is trained by solving a quadratic programming problem, which is expressed in the dual form as follows:
:\max_ \sum_^n \alpha_i - \frac12 \sum_^n \sum_^n y_i y_j K(x_i, x_j) \alpha_i \alpha_j,
:subject to:
:0 \leq \alpha_i \leq C, \quad \mbox i=1, 2, \ldots, n,
:\sum_^n y_i \alpha_i = 0
where ''C'' is an SVM hyperparameter and ''K''(''x''''i'', ''x''''j'') is the kernel function, both supplied by the user; and the variables \alpha_i are Lagrange multipliers.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
ウィキペディアで「Sequential minimal optimization」の詳細全文を読む



スポンサード リンク
翻訳と辞書 : 翻訳のためのインターネットリソース

Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.