Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers

• Item type: Trans(1)
• 公開日: 2011-02-22
• タイトル: Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers
• タイトル / 言語: en
• タイトル: Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: Regular Paper - Research Paper
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Tokyo Institute of Technology
• 著者所属: Tokyo Institute of Technology／PRESTO, JST
• 著者所属: Gunma University
• 著者所属(英): en
• 著者所属(英): Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Tokyo Institute of Technology / PRESTO, JST
• 著者所属(英): en
• 著者所属(英): Gunma University
• 著者名: Jaak, Simm; Masashi, Sugiyama; Tsuyoshi, Kato
• 著者名(英): Jaak, Simm; Masashi, Sugiyama; Tsuyoshi, Kato
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12394973
• 書誌情報: IPSJ Transactions on Computer Vision and Applications（CVA）; 巻 3, p. 1-8, 発行日 2011-02-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-6695
• 出版者 / 言語: ja
• 出版者: 情報処理学会