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» Approximation Methods for Supervised Learning
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CVPR
2011
IEEE
13 years 6 months ago
Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation
We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. The proposed method could find diverse applications in co...
Zhiding Yu, Oscar Au, Ketan Tang
BMCBI
2010
118views more  BMCBI 2010»
13 years 10 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
ECAI
2000
Springer
14 years 2 months ago
Naive Bayes and Exemplar-based Approaches to Word Sense Disambiguation Revisited
Abstract. This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar–basedclassification, on the Word Sens...
Gerard Escudero, Lluís Màrquez, Germ...
ICML
1995
IEEE
14 years 10 months ago
Stable Function Approximation in Dynamic Programming
The success ofreinforcement learninginpractical problems depends on the ability to combine function approximation with temporal di erence methods such as value iteration. Experime...
Geoffrey J. Gordon
SIGIR
2009
ACM
14 years 4 months ago
An improved markov random field model for supporting verbose queries
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...
Matthew Lease