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PKDD
2004
Springer
141views Data Mining» more  PKDD 2004»
14 years 27 days ago
Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by s...
Michelangelo Ceci, Annalisa Appice, Donato Malerba
NIPS
2004
13 years 9 months ago
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging
We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss funct...
Vladimir Koltchinskii, Manel Martínez-Ram&o...
INTERSPEECH
2010
13 years 2 months ago
A factorial sparse coder model for single channel source separation
We propose a probabilistic factorial sparse coder model for single channel source separation in the magnitude spectrogram domain. The mixture spectrogram is assumed to be the sum ...
Robert Peharz, Michael Stark, Franz Pernkopf, Yann...
JMLR
2010
163views more  JMLR 2010»
13 years 2 months ago
Dense Message Passing for Sparse Principal Component Analysis
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Kevin Sharp, Magnus Rattray
ICTAI
2008
IEEE
14 years 1 months ago
Classifying Spend Descriptions with Off-the-Shelf Learning Components
Analyzing spend transactions is essential to organizations for understanding their global procurement. Central to this analysis is the automated classification of these transacti...
Saikat Mukherjee, Dmitriy Fradkin, Michael Roth