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ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
13 years 10 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
COGSCI
2007
79views more  COGSCI 2007»
13 years 10 months ago
Contextualizing Counterintuitiveness: How Context Affects Comprehension and Memorability of Counterintuitive Concepts
A number of anthropologists have argued that religious concepts are minimally counterintuitive and that this gives them mnemic advantages. This paper addresses the question of why...
M. Afzal Upala, Lauren O. Gonce, Ryan D. Tweney, D...
PAMI
2006
206views more  PAMI 2006»
13 years 10 months ago
MILES: Multiple-Instance Learning via Embedded Instance Selection
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
Yixin Chen, Jinbo Bi, James Ze Wang
CORR
2002
Springer
91views Education» more  CORR 2002»
13 years 10 months ago
Data Engineering for the Analysis of Semiconductor Manufacturing Data
We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules ...
Peter D. Turney
SAJ
2002
105views more  SAJ 2002»
13 years 10 months ago
Hierarchical level of detail optimization for constant frame rate rendering of radiosity scenes
The predictive hierarchical level of detail optimization algorithm of Mason and Blake is experimentally evaluated in the form of a practical application to hierarchical radiosity....
Shaun Nirenstein, Edwin H. Blake, Simon Winberg, A...