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» An Instance Selection Approach to Multiple Instance Learning
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GECCO
2008
Springer
238views Optimization» more  GECCO 2008»
14 years 11 months ago
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values...
Antonio LaTorre, José Manuel Peña, V...
DGO
2008
126views Education» more  DGO 2008»
14 years 11 months ago
Active learning for e-rulemaking: public comment categorization
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
Stephen Purpura, Claire Cardie, Jesse Simons
GLVLSI
2005
IEEE
133views VLSI» more  GLVLSI 2005»
15 years 3 months ago
Generating decision regions in analog measurement spaces
We develop a neural network that learns to separate the nominal from the faulty instances of a circuit in a measurement space. We demonstrate that the required separation boundari...
Haralampos-G. D. Stratigopoulos, Yiorgos Makris
PKDD
2001
Springer
120views Data Mining» more  PKDD 2001»
15 years 2 months ago
Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation
We present a method for distinguishing two subtly different mental states, on the basis of the underlying brain activation measured with fMRI. The method uses a classifier to lea...
Francisco Pereira, Marcel Just, Tom M. Mitchell
AAAI
2006
14 years 11 months ago
Thresholding for Making Classifiers Cost-sensitive
In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The meth...
Victor S. Sheng, Charles X. Ling