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» Algorithm Selection using Reinforcement Learning
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TCS
2010
15 years 20 days ago
Active learning in heteroscedastic noise
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
András Antos, Varun Grover, Csaba Szepesv&a...
101
Voted
PR
2007
96views more  PR 2007»
15 years 1 months ago
Weighted and robust learning of subspace representations
A reliable system for visual learning and recognition should enable a selective treatment of individual parts of input data and should successfully deal with noise and occlusions....
Danijel Skocaj, Ales Leonardis, Horst Bischof
IROS
2006
IEEE
121views Robotics» more  IROS 2006»
15 years 8 months ago
Planning and Acting in Uncertain Environments using Probabilistic Inference
— An important problem in robotics is planning and selecting actions for goal-directed behavior in noisy uncertain environments. The problem is typically addressed within the fra...
Deepak Verma, Rajesh P. N. Rao
AIPS
2006
15 years 3 months ago
Optimal STRIPS Planning by Maximum Satisfiability and Accumulative Learning
Planning as satisfiability (SAT-Plan) is one of the best approaches to optimal planning, which has been shown effective on problems in many different domains. However, the potenti...
Zhao Xing, Yixin Chen, Weixiong Zhang
125
Voted
KES
2007
Springer
15 years 2 months ago
An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction
The study described in this paper, analyzed the urban and suburban air pollution principal causes and identified the best subset of features (meteorological data and air pollutants...
Giovanni Raimondo, Alfonso Montuori, Walter Moniac...