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ICML
2004
IEEE
15 years 10 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
PAMI
2012
13 years 6 days ago
Task-Driven Dictionary Learning
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Julien Mairal, Francis Bach, Jean Ponce
PPSN
2004
Springer
15 years 3 months ago
A Neuroevolutionary Approach to Emergent Task Decomposition
A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectu...
Jekanthan Thangavelautham, Gabriele M. T. D'Eleute...
CIARP
2009
Springer
15 years 1 months ago
Pigmented Skin Lesions Classification Using Dermatoscopic Images
In this paper we propose a machine learning approach to classify melanocytic lesions in malignant and benign from dermatoscopic images. The image database is composed of 433 benign...
Germán Capdehourat, Andrés Corez, An...
ACL
2009
14 years 7 months ago
Phrase Clustering for Discriminative Learning
We present a simple and scalable algorithm for clustering tens of millions of phrases and use the resulting clusters as features in discriminative classifiers. To demonstrate the ...
Dekang Lin, Xiaoyun Wu