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ICASSP
2011
IEEE
12 years 11 months ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
MAGS
2007
168views more  MAGS 2007»
13 years 7 months ago
Agent-based protein structure prediction
A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting...
Luca Bortolussi, Agostino Dovier, Federico Fogolar...
BMCBI
2007
113views more  BMCBI 2007»
13 years 7 months ago
Learning biophysically-motivated parameters for alpha helix prediction
Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure predict...
Blaise Gassend, Charles W. O'Donnell, William Thie...
IJCNN
2008
IEEE
14 years 1 months ago
On the learning of nonlinear visual features from natural images by optimizing response energies
— The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortic...
Jussi T. Lindgren, Aapo Hyvärinen
CORR
2006
Springer
127views Education» more  CORR 2006»
13 years 7 months ago
Semi-Supervised Learning -- A Statistical Physics Approach
We present a novel approach to semisupervised learning which is based on statistical physics. Most of the former work in the field of semi-supervised learning classifies the point...
Gad Getz, Noam Shental, Eytan Domany