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8月24日学术报告(刘威威,澳大利亚新南威尔士大学)
2018/8/22 9:19:39 人评论

报告题目:   Advanced Topics in Multi-label Learning

报告时间:   2018824日(周五)上午1000

报告地点:   计算机学院B403

报告人:     刘威威博士

报告人单位: 澳大利亚新南威尔士大学

 

报告人简介:

刘威威,现于澳大利亚新南威尔士大学从事博士后研究工作,于20178月获得悉尼科技大学(UTS)博士学位,导师是Ivor W.Tsang教授。主要研究方向包括多标签学习、聚类、特征选择和稀疏学习等,已在世界顶级期刊及会议上发表CCF A类一作学术论文10余篇,主要包括机器学习旗舰型学术期刊Journal of Machine Learning Research (JMLR),模式识别、计算机视觉和机器学习应用顶级期刊IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) ,机器学习顶级学术会议 NIPS,人工智能顶级学术会议AAAIIJCAI等。2017年获得中国留学基金委“优秀自费留学生”奖学金。

 

报告摘要

Multi-label learning, in which each instance can belong to multiple labels simultaneously, has significantly attracted the attention of researchers as a result of its wide range of applications, which range from document classification and automatic image annotation to video annotation. Many multi-label learning models have been developed to capture label dependency. Amongst them, the classifier chain (CC) model is one of the most popular methods due to its simplicity and promising experimental results. However, CC suffers from three important problems: Does the label order affect the performance of CC? Is there any globally optimal classifier chain which can achieve the optimal prediction performance for CC? If yes, how can the globally optimal classifier chain be found? It is non-trivial to answer these problems. Another important branch of methods for capturing label dependency is encoding-decoding paradigm. Based on structural SVMs, maximum margin output coding (MMOC) has become one of the most representative encoding-decoding methods and shown promising results for multi-label classification. Unfortunately, MMOC suffers from two major limitations: 1) Inconsistent performance. 2) Prohibitive computational cost. Therefore, it is non-trivial to break the bottlenecks of MMOC, and develop efficient and consistent algorithms for solving multi-label learning tasks. The prediction of most multi-label learning methods either scales linearly with the number of labels or involves an expensive decoding process, which usually requires solving a combinatorial optimization. Such approaches become unacceptable when tackling thousands of labels. It is imperative to design an efficient, yet accurate multi-label learning algorithm with the minimum number of predictions. This report systematically shows how to solve aforementioned issues.

 

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