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MIR
2005
ACM
129views Multimedia» more  MIR 2005»
14 years 4 months ago
Tracking concept drifting with an online-optimized incremental learning framework
Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
Jun Wu, Dayong Ding, Xian-Sheng Hua, Bo Zhang
TKDE
2012
226views Formal Methods» more  TKDE 2012»
12 years 1 months ago
DDD: A New Ensemble Approach for Dealing with Concept Drift
—Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines a...
Leandro L. Minku, Xin Yao
ECML
2007
Springer
14 years 2 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
SODA
2010
ACM
175views Algorithms» more  SODA 2010»
14 years 8 months ago
An Online Scalable Algorithm for Average Flow Time in Broadcast Scheduling
In this paper the online pull-based broadcast model is considered. In this model, there are n pages of data stored at a server and requests arrive for pages online. When the serve...
Benjamin Moseley, Sungjin Im
WWW
2009
ACM
14 years 11 months ago
Advertising keyword generation using active learning
This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a...
Hao Wu, Guang Qiu, Xiaofei He, Yuan Shi, Mingcheng...