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JCB
2002
70views more  JCB 2002»
9 years 3 months ago
Strong Feature Sets from Small Samples
For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
AAAI
1996
9 years 5 months ago
Bagging, Boosting, and C4.5
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classi er learning systems. Both form a set of classi ers t...
J. Ross Quinlan
IPPS
1999
IEEE
9 years 8 months ago
High-Performance Knowledge Extraction from Data on PC-Based Networks of Workstations
The automatic construction of classi ers programs able to correctly classify data collected from the real world is one of the major problems in pattern recognition and in a wide ar...
Cosimo Anglano, Attilio Giordana, Giuseppe Lo Bell...
ICDAR
2009
IEEE
9 years 10 months ago
Generic Feature Selection and Document Processing
This paper presents a generic features selection method and its applications on some document analysis problems. The method is based on a genetic algorithm (GA), whose tness funct...
Hassan Chouaib, Nicole Vincent, Florence Cloppet, ...
ICML
1998
IEEE
10 years 4 months ago
The Case against Accuracy Estimation for Comparing Induction Algorithms
We analyze critically the use of classi cation accuracy to compare classi ers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learnin...
Foster J. Provost, Tom Fawcett, Ron Kohavi
ICML
1999
IEEE
10 years 4 months ago
The Alternating Decision Tree Learning Algorithm
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
Yoav Freund, Llew Mason
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