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» A parallel learning algorithm for text classification
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KDD
2007
ACM
139views Data Mining» more  KDD 2007»
15 years 10 months ago
Raising the baseline for high-precision text classifiers
Many important application areas of text classifiers demand high precision and it is common to compare prospective solutions to the performance of Naive Bayes. This baseline is us...
Aleksander Kolcz, Wen-tau Yih
73
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ESWA
2006
149views more  ESWA 2006»
14 years 9 months ago
An effective refinement strategy for KNN text classifier
Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an...
Songbo Tan
SIGIR
2002
ACM
14 years 9 months ago
Probabilistic combination of text classifiers using reliability indicators: models and results
The intuition that different text classifiers behave in qualitatively different ways has long motivated attempts to build a better metaclassifier via some combination of classifie...
Paul N. Bennett, Susan T. Dumais, Eric Horvitz
ICDM
2010
IEEE
147views Data Mining» more  ICDM 2010»
14 years 7 months ago
Location and Scatter Matching for Dataset Shift in Text Mining
Dataset shift from the training data in a source domain to the data in a target domain poses a great challenge for many statistical learning methods. Most algorithms can be viewed ...
Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong
ICML
2003
IEEE
15 years 10 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty