图 书 馆开课单位--微软公司
21
22
23
24
25
26
27
28
29
30
2122Learning in the Loop[在循环学习]
John Langford(微软公司) Learning from user feedback well requires solving several problems simultaneously:\\ {a) Dealing with the scale of user feedback, which is often sever...
热度：50
John Langford(微软公司) Learning from user feedback well requires solving several problems simultaneously:\\ {a) Dealing with the scale of user feedback, which is often sever...
热度：50
23Understanding Temporal Query Dynamics[理解时态查询动态]
Krysta M. Svore(微软公司) Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., eart...
热度：35
Krysta M. Svore(微软公司) Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., eart...
热度：35
24Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders[差异私人推荐系统：建立保密到Netflix的奖竞争者]
Frank McSherry(微软公司) We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these user...
热度：112
Frank McSherry(微软公司) We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these user...
热度：112
25A Social Network Approach to Unsupervised Induction of Syntactic Clusters for Bengali[孟加拉语单词聚类无监督引导进行归纳的社会网络方法]
Monojit Choudhury(微软公司) In this paper we describe some experiments on fully unsupervised induction of parts-of-speech tags for Bengali words from a raw text corpus. For this ...
热度：73
Monojit Choudhury(微软公司) In this paper we describe some experiments on fully unsupervised induction of parts-of-speech tags for Bengali words from a raw text corpus. For this ...
热度：73
26Multiple Kernel Learning and the SMO Algorithm[多核学习与SMO算法]
Manik Varma(微软公司) Our objective is to train p-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Seque...
热度：71
Manik Varma(微软公司) Our objective is to train p-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Seque...
热度：71
27The Offset Tree for Learning with Partial Labels [偏移树与部分标签学习]
John Langford(微软公司) We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather ...
热度：54
John Langford(微软公司) We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather ...
热度：54
28Going Global with Mobile App Development: Enabling the Connected Enterprise[走出去与手机应用开发：使关联企业]
Jan Anders Nelson(微软公司) Companies are shifting from viewing the enterprise as a machine with mostly sequential workflows to seeing it as an interconnected complex ecosystem w...
热度：44
Jan Anders Nelson(微软公司) Companies are shifting from viewing the enterprise as a machine with mostly sequential workflows to seeing it as an interconnected complex ecosystem w...
热度：44
29Understanding and Predicting Personal Navigation[了解和预测个人导航]
Jaime Teevan(微软公司) This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Pre...
热度：41
Jaime Teevan(微软公司) This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Pre...
热度：41
30