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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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111
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COLT
2004
Springer
15 years 4 months ago
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce an incremental algorithm u...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...
88
Voted
INFOCOM
2009
IEEE
15 years 7 months ago
Event Recognition in Sensor Networks by Means of Grammatical Inference
Abstract—Modern military and civilian surveillance applications should provide end users with the high level representation of events observed by sensors rather than with the raw...
Sahin Cem Geyik, Boleslaw K. Szymanski
135
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IJON
2006
131views more  IJON 2006»
15 years 15 days ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
GECCO
2004
Springer
144views Optimization» more  GECCO 2004»
15 years 6 months ago
Feature Subset Selection, Class Separability, and Genetic Algorithms
Abstract. The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore,...
Erick Cantú-Paz
ICPR
2004
IEEE
16 years 1 months ago
Switching Particle Filters for Efficient Real-time Visual Tracking
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series signals distributed by non-Gaussian noise. A couple of variant par...
Kenji Doya, Shin Ishii, Takashi Bando, Tomohiro Sh...