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. 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.

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dbo:abstract
  • L'Ottimizzazione minima sequenziale (in inglese: Sequential minimal optimization, in sigla SMO) è un algoritmo per risolvere efficientemente il problema di ottimizzazione che emerge durante l'addestramento di una Macchine a vettori di supporto.Fu inventato da John Platt nel 1998 al laboratorio Microsoft Research di Redmond.L'Ottimizzazione minima sequenziale è implementata nella famosa libreria software libsvm. (it)
  • 逐次最小問題最適化法(英: Sequential Minimal Optimization, SMO)はサポートベクターマシン(SVM)の訓練で生じる2次計画問題(QP)を解くためのアルゴリズムである。1998年にマイクロソフトリサーチのJohn Plattによって発明された。SMOはサポートベクターマシンの訓練のために広く使われ、人気のLIBSVMツールによって実装される。以前から利用できたSVM訓練法はより一層複雑で、高価なサードパーティのQPソルバーを必要としたので、1998年のSMOアルゴリズムの公表はSVMコミュニティでたくさんの興奮を引き起こした。 (ja)
  • 序列最小优化算法(英语:Sequential minimal optimization, SMO)是一种用于解决支持向量机训练过程中所产生优化问题的算法。SMO由微软研究院的约翰·普莱特于1998年发明,目前被广泛使用于SVM的训练过程中,并在通行的SVM库LIBSVM中得到实现。 1998年,SMO算法发表在SVM研究领域内引起了轰动,因为先前可用的SVM训练方法必须使用复杂的方法,并需要昂贵的第三方二次规划工具。而SMO算法较好地避免了这一问题。 (zh)
  • 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. 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. (en)
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  • Optimization algorithm for training support vector machines
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  • O
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http://purl.org/linguistics/gold/hypernym
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rdfs:comment
  • L'Ottimizzazione minima sequenziale (in inglese: Sequential minimal optimization, in sigla SMO) è un algoritmo per risolvere efficientemente il problema di ottimizzazione che emerge durante l'addestramento di una Macchine a vettori di supporto.Fu inventato da John Platt nel 1998 al laboratorio Microsoft Research di Redmond.L'Ottimizzazione minima sequenziale è implementata nella famosa libreria software libsvm. (it)
  • 逐次最小問題最適化法(英: Sequential Minimal Optimization, SMO)はサポートベクターマシン(SVM)の訓練で生じる2次計画問題(QP)を解くためのアルゴリズムである。1998年にマイクロソフトリサーチのJohn Plattによって発明された。SMOはサポートベクターマシンの訓練のために広く使われ、人気のLIBSVMツールによって実装される。以前から利用できたSVM訓練法はより一層複雑で、高価なサードパーティのQPソルバーを必要としたので、1998年のSMOアルゴリズムの公表はSVMコミュニティでたくさんの興奮を引き起こした。 (ja)
  • 序列最小优化算法(英语:Sequential minimal optimization, SMO)是一种用于解决支持向量机训练过程中所产生优化问题的算法。SMO由微软研究院的约翰·普莱特于1998年发明,目前被广泛使用于SVM的训练过程中,并在通行的SVM库LIBSVM中得到实现。 1998年,SMO算法发表在SVM研究领域内引起了轰动,因为先前可用的SVM训练方法必须使用复杂的方法,并需要昂贵的第三方二次规划工具。而SMO算法较好地避免了这一问题。 (zh)
  • 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. 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. (en)
rdfs:label
  • Ottimizzazione minima sequenziale (it)
  • 逐次最小問題最適化法 (ja)
  • 序列最小优化算法 (zh)
  • Sequential minimal optimization (en)
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