文章基本信息

标题：Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers
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作者：Jaak Simm ; Masashi Sugiyama ; Tsuyoshi Kato 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2011
卷号：6
期号：2
页码：508-515
DOI：10.11185/imt.6.508
出版社：Information and Media Technologies Editorial Board
摘要：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.