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ICML
2005
IEEE
16 years 3 months ago
A model for handling approximate, noisy or incomplete labeling in text classification
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties associated with the labeling process. Given a labeled or partially labeled training corpus of...
Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Ra...
JMLR
2008
117views more  JMLR 2008»
15 years 2 months ago
Closed Sets for Labeled Data
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with ...
Gemma C. Garriga, Petra Kralj, Nada Lavrac
ICML
2003
IEEE
16 years 3 months ago
Identifying Predictive Structures in Relational Data Using Multiple Instance Learning
This paper introduces an approach for identifying predictive structures in relational data using the multiple-instance framework. By a predictive structure, we mean a structure th...
Amy McGovern, David Jensen
AUSDM
2008
Springer
243views Data Mining» more  AUSDM 2008»
15 years 4 months ago
Structure-Based Document Model with Discrete Wavelet Transforms and Its Application to Document Classification
Term signal is an existing text representation that depicts a term as a vector of frequencies of occurrences in a number of user-defined partitions of a document. Although term si...
Supphachai Thaicharoen, Tom Altman, Krzysztof J. C...
ICCV
2003
IEEE
16 years 4 months ago
Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification
In this work we present Discriminative Random Fields (DRFs), a discriminative framework for the classification of image regions by incorporating neighborhood interactions in the l...
Sanjiv Kumar, Martial Hebert