1.海量数据的主题模型(Topic-model on large scale data)
2.推荐系统(recommender system)
3.高效训练样本获取(Label Complexity Reduction)
4.基于大规模机器学习的排序算法(Machine Learning to Rank)
5.点击模型(Click Model)
6.网页多分类学习
7.基于海量网页的结构化信息自动抽取研究
8.观点挖掘、情感分析(opinion mining and sentiment analysis)
9.规则系统与机器学习系统的整合(integrating rule-based system and learning-based system)
10.海量特征设计(large scale feature engineering)
11.基于机器学习的反作弊研究(fraud detection based on machine learning)
2.推荐系统(recommender system)
3.高效训练样本获取(Label Complexity Reduction)
4.基于大规模机器学习的排序算法(Machine Learning to Rank)
5.点击模型(Click Model)
6.网页多分类学习
7.基于海量网页的结构化信息自动抽取研究
8.观点挖掘、情感分析(opinion mining and sentiment analysis)
9.规则系统与机器学习系统的整合(integrating rule-based system and learning-based system)
10.海量特征设计(large scale feature engineering)
11.基于机器学习的反作弊研究(fraud detection based on machine learning)
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