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» Robust Learning - Rich and Poor
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135
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ICML
2004
IEEE
16 years 2 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
ECML
2005
Springer
15 years 7 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
136
Voted
ICML
2010
IEEE
15 years 2 months ago
Sequential Projection Learning for Hashing with Compact Codes
Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing metho...
Jun Wang, Sanjiv Kumar, Shih-Fu Chang
114
Voted
JMLR
2010
95views more  JMLR 2010»
14 years 8 months ago
Feature Extraction for Machine Learning: Logic-Probabilistic Approach
The paper analyzes peculiarities of preprocessing of learning data represented in object data bases constituted by multiple relational tables with ontology on top of it. Exactly s...
Vladimir Gorodetsky, Vladimir Samoilov
ICML
2007
IEEE
16 years 2 months ago
Robust mixtures in the presence of measurement errors
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is d...
Ata Kabán, Jianyong Sun, Somak Raychaudhury