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ICIP
2003
IEEE
16 years 6 months ago
Image compression with on-line and off-line learning
Images typically contain smooth regions, which are easily compressed by linear transforms, and high activity regions (edges, textures), which are harder to compress. To compress t...
Patrice Y. Simard, Christopher J. C. Burges, David...
CORR
2010
Springer
103views Education» more  CORR 2010»
15 years 4 months ago
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such ru...
Joseph Shtok, Michael Zibulevsky, Michael Elad
ACTAC
2006
126views more  ACTAC 2006»
15 years 4 months ago
Named Entity Recognition for Hungarian Using Various Machine Learning Algorithms
In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns...
Richárd Farkas, György Szarvas, Andr&a...
DAGSTUHL
2009
15 years 5 months ago
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Erzsébet Merényi, Kadim Tasdemir, Li...
NN
2004
Springer
102views Neural Networks» more  NN 2004»
15 years 10 months ago
A Quantitative Evaluation of a Bio-inspired Sound Segregation Technique for Two- and Three-Source Mixtures
A sound source separation technique based on a bio-inspired neural network, capable of functioning in more than two-source mixtures, is proposed. Separation results are compared wi...
Ramin Pichevar, Jean Rouat