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NAACL
2003
13 years 8 months ago
Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Shona Douglas
GECCO
2010
Springer
155views Optimization» more  GECCO 2010»
14 years 5 days ago
Negative selection algorithms without generating detectors
Negative selection algorithms are immune-inspired classifiers that are trained on negative examples only. Classification is performed by generating detectors that match none of ...
Maciej Liskiewicz, Johannes Textor
SAC
2003
ACM
14 years 19 days ago
Supervised Term Weighting for Automated Text Categorization
The construction of a text classifier usually involves (i) a phase of term selection, in which the most relevant terms for the classification task are identified, (ii) a phase ...
Franca Debole, Fabrizio Sebastiani
ICPR
2006
IEEE
14 years 8 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
NPL
1998
129views more  NPL 1998»
13 years 7 months ago
Extraction of Logical Rules from Neural Networks
A new architecture and method for feature selection and extraction of logical rules from neural networks trained with backpropagation algorithm is presented. The network consists ...
Wlodzislaw Duch, Rafal Adamczak, Krzysztof Grabcze...