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128
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ICASSP
2008
IEEE
15 years 10 months ago
Learning to satisfy
This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies...
Frederic Thouin, Mark Coates, Brian Eriksson, Robe...
179
Voted
KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 4 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
119
Voted
ICML
2003
IEEE
16 years 4 months ago
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...
Wee Sun Lee, Bing Liu
115
Voted
CHI
2009
ACM
16 years 4 months ago
Designing CALLY, : a cell-phone robot
This proposal describes the early phase of our design process developing a robot cell-phone named CALLY, with which we are exploring the roles of facial and gestural expressions o...
Ji-Dong Yim, Christopher D. Shaw
119
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FLAIRS
2004
15 years 5 months ago
Case-Based Bayesian Network Classifiers
We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
Eugene Santos, Ahmed Huessin