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IJCNN
2006
IEEE
14 years 1 months ago
Learning to Segment Any Random Vector
— We propose a method that takes observations of a random vector as input, and learns to segment each observation into two disjoint parts. We show how to use the internal coheren...
Aapo Hyvärinen, Jukka Perkiö
CORR
2006
Springer
99views Education» more  CORR 2006»
13 years 7 months ago
Logical settings for concept learning from incomplete examples in First Order Logic
We investigate here concept learning from incomplete examples. Our first purpose is to discuss to what extent logical learning settings have to be modified in order to cope with da...
Dominique Bouthinon, Henry Soldano, Véroniq...
FOCS
2008
IEEE
14 years 2 months ago
Learning Geometric Concepts via Gaussian Surface Area
We study the learnability of sets in Rn under the Gaussian distribution, taking Gaussian surface area as the “complexity measure” of the sets being learned. Let CS denote the ...
Adam R. Klivans, Ryan O'Donnell, Rocco A. Servedio
ICONIP
1998
13 years 9 months ago
Inducing Relational Concepts with Neural Networks via the LINUS System
This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken...
Rodrigo Basilio, Gerson Zaverucha, Artur S. d'Avil...
TSP
2008
124views more  TSP 2008»
13 years 7 months ago
Dictionary Preconditioning for Greedy Algorithms
This article introduces the concept of sensing dictionaries. It presents an alteration of greedy algorithms like thresholding or (Orthogonal) Matching Pursuit which improves their...
Karin Schnass, Pierre Vandergheynst