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» Improved Guarantees for Learning via Similarity Functions
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CVPR
2008
IEEE
14 years 7 months ago
Semi-supervised boosting using visual similarity learning
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semisupervise...
Christian Leistner, Helmut Grabner, Horst Bischof
ICML
2006
IEEE
14 years 6 months ago
Ranking on graph data
In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or order...
Shivani Agarwal
ATAL
2005
Springer
13 years 10 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
ICML
2007
IEEE
14 years 6 months ago
Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation
Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
Chee Wee Phua, Robert Fitch
JMLR
2006
90views more  JMLR 2006»
13 years 5 months ago
Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
We present worst case bounds for the learning rate of a known prediction method that is based on hierarchical applications of binary context tree weighting (CTW) predictors. A heu...
Ron Begleiter, Ran El-Yaniv