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ICML
1999
IEEE
16 years 5 months ago
Machine-Learning Applications of Algorithmic Randomness
Most machine learning algorithms share the following drawback: they only output bare predictions but not the con dence in those predictions. In the 1960s algorithmic information t...
Volodya Vovk, Alexander Gammerman, Craig Saunders
IJON
2008
133views more  IJON 2008»
15 years 3 months ago
A multi-objective approach to RBF network learning
The problem of inductive supervised learning is discussed in this paper within the context of multi-objective (MOBJ) optimization. The smoothness-based apparent (effective) comple...
Illya Kokshenev, Antônio de Pádua Bra...
117
Voted
TSMC
2002
136views more  TSMC 2002»
15 years 4 months ago
Expertness based cooperative Q-learning
By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be ...
Majid Nili Ahmadabadi, Masoud Asadpour
158
Voted
ECCV
2006
Springer
15 years 8 months ago
Learning Semantic Scene Models by Trajectory Analysis
Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of mo...
Xiaogang Wang, Kinh Tieu, Eric Grimson
132
Voted
INFOCOM
2008
IEEE
15 years 11 months ago
Minerva: Learning to Infer Network Path Properties
—Knowledge of the network path properties such as latency, hop count, loss and bandwidth is key to the performance of overlay networks, grids and p2p applications. Network operat...
Rita H. Wouhaybi, Puneet Sharma, Sujata Banerjee, ...