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- SAIGO Hiroto
- Kyushu Institute of Technology
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- KASHIMA Hisashi
- University of Tokyo
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- TSUDA Koji
- Advanced Industrial Science and Technology
説明
Apriori-based mining algorithms enumerate frequent patterns efficiently, but the resulting large number of patterns makes it difficult to directly apply subsequent learning tasks. Recently, efficient iterative methods are proposed for mining discriminative patterns for classification and regression. These methods iteratively execute discriminative pattern mining algorithm and update example weights to emphasize on examples which received large errors in the previous iteration. In this paper, we study a family of loss functions that induces sparsity on example weights. Most of the resulting example weights become zeros, so we can eliminate those examples from discriminative pattern mining, leading to a significant decrease in search space and time. In computational experiments we compare and evaluate various loss functions in terms of the amount of sparsity induced and resulting speed-up obtained.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E96.D (8), 1766-1773, 2013
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390001204377986560
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- NII論文ID
- 130003370958
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可