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IFIP12
2008
15 years 6 months ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unse...
Frederic T. Stahl, Max A. Bramer, Mo Adda
UAI
2008
15 years 6 months ago
Small Sample Inference for Generalization Error in Classification Using the CUD Bound
Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
Eric Laber, Susan Murphy
CORR
2010
Springer
143views Education» more  CORR 2010»
15 years 4 months ago
Algorithmic Detection of Computer Generated Text
ct Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such do...
Allen Lavoie, Mukkai Krishnamoorthy
ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
15 years 4 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
EMNLP
2010
15 years 2 months ago
Negative Training Data Can be Harmful to Text Classification
This paper studies the effects of training data on binary text classification and postulates that negative training data is not needed and may even be harmful for the task. Tradit...
Xiaoli Li, Bing Liu, See-Kiong Ng