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» Predicting relative performance of classifiers from samples
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SETN
2004
Springer
14 years 1 months ago
A Meta-classifier Approach for Medical Diagnosis
Abstract. Single classifiers, such as Neural Networks, Support Vector Machines, Decision Trees and other, can be used to perform classification of data for relatively simple proble...
George L. Tsirogiannis, Dimitrios S. Frossyniotis,...
ICMLA
2010
13 years 5 months ago
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...
TIT
2010
96views Education» more  TIT 2010»
13 years 2 months ago
Beyond Nyquist: efficient sampling of sparse bandlimited signals
Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficie...
Joel A. Tropp, Jason N. Laska, Marco F. Duarte, Ju...
BMCBI
2008
178views more  BMCBI 2008»
13 years 7 months ago
A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic ana
Background: Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) ...
Bin Liu, Xiaolong Wang, Lei Lin, Qiwen Dong, Xuan ...
NIPS
2007
13 years 9 months ago
Locality and low-dimensions in the prediction of natural experience from fMRI
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels d...
Francois Meyer, Greg Stephens