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» Map Labeling and Its Generalizations
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BMCBI
2002
131views more  BMCBI 2002»
13 years 7 months ago
Efficient Boolean implementation of universal sequence maps (bUSM)
Background: Recently, Almeida and Vinga offered a new approach for the representation of arbitrary discrete sequences, referred to as Universal Sequence Maps (USM), and discussed ...
John Schwacke, Jonas S. Almeida
ICML
2010
IEEE
13 years 8 months ago
Non-Local Contrastive Objectives
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller
CIKM
2010
Springer
13 years 6 months ago
Rank learning for factoid question answering with linguistic and semantic constraints
This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features. The framework enables query...
Matthew W. Bilotti, Jonathan L. Elsas, Jaime G. Ca...
TKDE
2010
182views more  TKDE 2010»
13 years 6 months ago
MILD: Multiple-Instance Learning via Disambiguation
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...
Wu-Jun Li, Dit-Yan Yeung
DIS
2009
Springer
14 years 2 months ago
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
Yasser El-Manzalawy, Vasant Honavar