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IJCAI
2003
13 years 10 months ago
Hierarchical Hidden Markov Models for Information Extraction
Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learni...
Marios Skounakis, Mark Craven, Soumya Ray
ECCV
2010
Springer
13 years 10 months ago
Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision
The saddle point framework provides a convenient way to formulate many convex variational problems that occur in computer vision. The framework unifies a broad range of data and re...
Jan Lellmann, Dirk Breitenreicher, Christoph Schn&...
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 10 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...
GECCO
2008
Springer
168views Optimization» more  GECCO 2008»
13 years 10 months ago
Parameterizing pair approximations for takeover dynamics
Pair approximations have often been used to predict equilibrium conditions in spatially-explicit epidemiological and ecological systems. In this work, we investigate whether this ...
Joshua L. Payne, Margaret J. Eppstein
ICASSP
2010
IEEE
13 years 9 months ago
Ultrasound tomography with learned dictionaries
We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scanners. Given the measurements, our algorithm finds a...
Ivana Tosic, Ivana Jovanovic, Pascal Frossard, Mar...