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HIS
2004
13 years 11 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...
CLIN
2001
13 years 11 months ago
Memory-Based Phoneme-to-Grapheme Conversion
In this paper, we describe a method to enhance the readability of out-of-vocabulary items (OOVs) in the textual output in a large vocabulary continuous speech recognition system. ...
Bart Decadt, Jacques Duchateau, Walter Daelemans, ...
IJCAI
2003
13 years 11 months ago
Visual Programming of Web Data Aggregation Applications
Most of the information needs today can be satisfied by searching and browsing the Web. However, repetitive tasks such as monitoring information on Web sites should be done autom...
Robert Baumgartner, Georg Gottlob, Marcus Herzog
AAAI
1998
13 years 11 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans
AVI
2000
13 years 11 months ago
Diagrams Based on Structural Object Perception
Most diagrams, particularly those used in software engineering, are line drawings consisting of nodes drawn as rectangles or circles, and edges drawn as lines linking them. In the...
Pourang Irani, Colin Ware