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ICML
2010
IEEE
13 years 9 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
PKDD
2010
Springer
152views Data Mining» more  PKDD 2010»
13 years 7 months ago
Online Knowledge-Based Support Vector Machines
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee...
ICML
2005
IEEE
14 years 9 months ago
Predicting relative performance of classifiers from samples
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
Rui Leite, Pavel Brazdil
COLT
2008
Springer
13 years 10 months ago
Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs
Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that ...
Elad Hazan, Satyen Kale
MLDM
2007
Springer
14 years 2 months ago
Discovering Plausible Explanations of Carcinogenecity in Chemical Compounds
Abstract. The goal of predictive toxicology is the automatic construction of carcinogenecity models. Most common artificial intelligence techniques used to construct these models ...
Eva Armengol