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» The Inefficiency of Batch Training for Large Training Sets
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ICPR
2000
IEEE
14 years 1 months ago
Scaling-Up Support Vector Machines Using Boosting Algorithm
In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadrati...
Dmitry Pavlov, Jianchang Mao, Byron Dom
HIS
2004
13 years 10 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...
ICPP
2008
IEEE
14 years 3 months ago
Dynamic Meta-Learning for Failure Prediction in Large-Scale Systems: A Case Study
Despite great efforts on the design of ultra-reliable components, the increase of system size and complexity has outpaced the improvement of component reliability. As a result, fa...
Jiexing Gu, Ziming Zheng, Zhiling Lan, John White,...
KDD
2010
ACM
222views Data Mining» more  KDD 2010»
13 years 10 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
ECML
2004
Springer
14 years 2 months ago
Learning from Message Pairs for Automatic Email Answering
Abstract. We consider the problem of learning a mapping from question to answer messages. The training data for this problem consist of pairs of messages that have been received an...
Steffen Bickel, Tobias Scheffer