Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
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概要
- 論文の詳細を見る
Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as text categorization and image annotation. In multi-label scenarios, taking into account correlations among multiple labels can boost the classification accuracy. However, this makes classifier training more challenging because handling multiple labels induces a high-dimensional optimization problem. In this paper, we propose a scalable multi-label method based on the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.
- The Institute of Electronics, Information and Communication Engineersの論文
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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NAM Hyunha
Tokyo Institute of Technology
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