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PAMI
2006
215views more  PAMI 2006»
13 years 10 months ago
Bayesian Feature and Model Selection for Gaussian Mixture Models
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
ECCV
2008
Springer
14 years 12 months ago
Feature Correspondence Via Graph Matching: Models and Global Optimization
Abstract. In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter an...
Lorenzo Torresani, Vladimir Kolmogorov, Carsten Ro...
ICIP
2008
IEEE
14 years 11 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 10 months ago
On updates that constrain the features' connections during learning
In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We invest...
Omid Madani, Jian Huang 0002
SIGIR
2006
ACM
14 years 4 months ago
Feature diversity in cluster ensembles for robust document clustering
The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one ...
Xavier Sevillano, Germán Cobo, Francesc Al&...