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» A distributed machine learning framework
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109
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ICML
2008
IEEE
16 years 3 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
124
Voted
ICML
2009
IEEE
16 years 3 months ago
Good learners for evil teachers
We consider a supervised machine learning scenario where labels are provided by a heterogeneous set of teachers, some of which are mediocre, incompetent, or perhaps even malicious...
Ofer Dekel, Ohad Shamir
97
Voted
CORR
2008
Springer
118views Education» more  CORR 2008»
15 years 2 months ago
Learning Low-Density Separators
Abstract. We define a novel, basic, unsupervised learning problem learning the the lowest density homogeneous hyperplane separator of an unknown probability distribution. This task...
Shai Ben-David, Tyler Lu, Dávid Pál,...
108
Voted
ICPR
2010
IEEE
15 years 9 months ago
Adding Classes Online in Error Correcting Output Codes Framework
—This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework to the online learning scenario. As a result, the final classifier handles the ...
Sergio Escalera, David Masip, Eloi Puertas, Petia ...
114
Voted
ECML
2004
Springer
15 years 8 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner