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WSC
1997
14 years 11 months ago
Descriptive Sampling: An Improvement over Latin Hypercube Sampling
Descriptive Sampling (DS), a Monte Carlo sampling technique based on a deterministic selection of the input values and their random permutation, represents a deep conceptual chang...
Eduardo Saliby
ICCV
2003
IEEE
15 years 12 months ago
Entropy-of-likelihood Feature Selection for Image Correspondence
Feature points for image correspondence are often selected according to subjective criteria (e.g. edge density, nostrils). In this paper, we present a general, non-subjective crit...
Matthew Toews, Tal Arbel
NIPS
2007
14 years 11 months ago
Feature Selection Methods for Improving Protein Structure Prediction with Rosetta
Rosetta is one of the leading algorithms for protein structure prediction today. It is a Monte Carlo energy minimization method requiring many random restarts to find structures ...
Ben Blum, Michael I. Jordan, David Kim, Rhiju Das,...
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 1 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
WIRN
2005
Springer
15 years 3 months ago
Ensembles Based on Random Projections to Improve the Accuracy of Clustering Algorithms
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
Alberto Bertoni, Giorgio Valentini