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BMCBI
2008
103views more  BMCBI 2008»
13 years 7 months ago
Discovering multi-level structures in bio-molecular data through the Bernstein inequality
Background: The unsupervised discovery of structures (i.e. clusterings) underlying data is a central issue in several branches of bioinformatics. Methods based on the concept of s...
Alberto Bertoni, Giorgio Valentini
JPDC
2006
185views more  JPDC 2006»
13 years 7 months ago
Commodity cluster-based parallel processing of hyperspectral imagery
The rapid development of space and computer technologies has made possible to store a large amount of remotely sensed image data, collected from heterogeneous sources. In particul...
Antonio Plaza, David Valencia, Javier Plaza, Pablo...
PAMI
2008
161views more  PAMI 2008»
13 years 7 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
NN
1998
Springer
177views Neural Networks» more  NN 1998»
13 years 7 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
ICCV
2009
IEEE
15 years 21 days ago
Saliency Driven Total Variation Segmentation
This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part...
Michael Donoser, Martin Urschler, Martin Hirzer an...