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SIGIR
2012
ACM
13 years 9 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
ICML
2009
IEEE
16 years 7 months ago
Unsupervised hierarchical modeling of locomotion styles
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...
Wei Pan, Lorenzo Torresani
KDD
2008
ACM
182views Data Mining» more  KDD 2008»
16 years 7 months ago
Classification with partial labels
In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are...
Nam Nguyen, Rich Caruana
ATAL
2009
Springer
16 years 1 months ago
Solving multiagent assignment Markov decision processes
We consider the setting of multiple collaborative agents trying to complete a set of tasks as assigned by a centralized controller. We propose a scalable method called“Assignmen...
Scott Proper, Prasad Tadepalli
CEC
2007
IEEE
16 years 1 months ago
Evolving neuromodulatory topologies for reinforcement learning-like problems
— Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advanta...
Andrea Soltoggio, Peter Dürr, Claudio Mattius...