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NIPS
2001
13 years 9 months ago
Boosting and Maximum Likelihood for Exponential Models
We derive an equivalence between AdaBoost and the dual of a convex optimization problem, showing that the only difference between minimizing the exponential loss used by AdaBoost ...
Guy Lebanon, John D. Lafferty
SIGGRAPH
1997
ACM
13 years 11 months ago
Multiresolution sampling procedure for analysis and synthesis of texture images
This paper outlines a technique for treating input texture images as probability density estimators from which new textures, with similar appearance and structural properties, can...
Jeremy S. De Bonet
IIS
2000
13 years 9 months ago
Optimization and Interpretation of Rule-based Classifiers
Machine learning methods are frequently used to create rule-based classifiers. For continuous features linguistic variables used in conditions of the rules are defined by membershi...
Wlodzislaw Duch, Norbert Jankowski, Krzysztof Grab...
BMCBI
2008
122views more  BMCBI 2008»
13 years 7 months ago
Effects of dependence in high-dimensional multiple testing problems
Background: We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) contro...
Kyung In Kim, Mark A. van de Wiel
CLEF
2009
Springer
13 years 8 months ago
Unsupervised Word Decomposition with the Promodes Algorithm
We present Promodes, an algorithm for unsupervised word decomposition, which is based on a probabilistic generative model. The model considers segment boundaries as hidden variable...
Sebastian Spiegler, Bruno Golénia, Peter A....