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» Learning of Boolean Functions Using Support Vector Machines
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IJCNN
2006
IEEE
14 years 3 months ago
P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data
— The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a st...
Johannes Mohr, Imke Puis, Jana Wrase, Sepp Hochrei...
ICALP
2011
Springer
13 years 19 days ago
New Algorithms for Learning in Presence of Errors
We give new algorithms for a variety of randomly-generated instances of computational problems using a linearization technique that reduces to solving a system of linear equations...
Sanjeev Arora, Rong Ge
ICALT
2007
IEEE
14 years 3 months ago
The Design of e-Learning Environment Oriented for Personalized Adaptability
A design of e-Learning environment is described for personalized adaptability. At first, we explain the whole system of our learning management system, WebClass RAPSODY, which has...
Toshie Ninomiya, Ken Nakayama, Miyuki Shimizu, Fum...
NIPS
2001
13 years 10 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
ECML
2005
Springer
14 years 2 months ago
Rotational Prior Knowledge for SVMs
Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear supp...
Arkady Epshteyn, Gerald DeJong