Multi-task learning with least-squares probabilistic classifiers (情報論的学習理論と機械学習)
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概要
- 論文の詳細を見る
Probabilistic classification and multi-task learning are two important branches of machine learning research. Probabilistic classification is useful when the 'confidence' of decision is necessary. On the other hand, the idea of multi-task learning is beneficial if multiple related learning tasks exist. So far, kernelized logistic regression has been a vital probabilistic classifier for the use in multi-task learning scenarios. However, its training tends to be computationally expensive, which prevented its use in large-scale problems. To overcome this limitation, we propose to employ a recently-proposed probabilistic classifier called the least-squares probabilistic classifier in multi-task learning scenarios. Through image classification experiments, we show that our method achieves comparable classification performance to the existing method, with much less training time.
- 社団法人電子情報通信学会の論文
- 2010-08-29
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol. Tokyo Jpn
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SUGIYAMA Masashi
Department of Computer Science, Tokyo Institute of Technology
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SIMM Jaak
Department of Computer Science, Tokyo Institute of Technology
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KATO Tsuyoshi
Graduate School of Frontier Sciences, University of Tokyo
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Simm Jaak
Department Of Computer Science Tokyo Institute Of Technology
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Kato Tsuyoshi
Graduate School Of Frontier Sciences University Of Tokyo
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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Kato Tsuyoshi
Graduate School Of Frontier Sciences The University Of Tokyo : Aist Computational Biology Research Center
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