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» On learning algorithm selection for classification
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SBBD
2000
168views Database» more  SBBD 2000»
15 years 8 months ago
Fast Feature Selection Using Fractal Dimension
Dimensionalitycurse and dimensionalityreduction are two issues that have retained highinterest for data mining, machine learning, multimedia indexing, and clustering. We present a...
Caetano Traina Jr., Agma J. M. Traina, Leejay Wu, ...
DIS
2008
Springer
15 years 7 months ago
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
We consider learning tasks where multiple target variables need to be predicted. Two approaches have been used in this setting: (a) build a separate single-target model for each ta...
Beau Piccart, Jan Struyf, Hendrik Blockeel
CORR
2010
Springer
125views Education» more  CORR 2010»
15 years 6 months ago
Near-Optimal Bayesian Active Learning with Noisy Observations
We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypot...
Daniel Golovin, Andreas Krause, Debajyoti Ray
SEBD
2008
169views Database» more  SEBD 2008»
15 years 8 months ago
Clustering the Feature Space
Abstract Dino Ienco and Rosa Meo Dipartimento di Informatica, Universit`a di Torino, Italy In this paper we propose and test the use of hierarchical clustering for feature selectio...
Dino Ienco, Rosa Meo
164
Voted
ICML
1997
IEEE
16 years 7 months ago
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
Nir Friedman