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» Evaluating learning algorithms and classifiers
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ICMCS
2005
IEEE
221views Multimedia» more  ICMCS 2005»
14 years 4 months ago
A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
ICNC
2005
Springer
14 years 4 months ago
Applying Genetic Programming to Evolve Learned Rules for Network Anomaly Detection
The DARPA/MIT Lincoln Laboratory off-line intrusion detection evaluation data set is the most widely used public benchmark for testing intrusion detection systems. But the presence...
Chuanhuan Yin, Shengfeng Tian, Houkuan Huang, Jun ...
SSPR
1998
Springer
14 years 3 months ago
Modified Minimum Classification Error Learning and Its Application to Neural Networks
A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learn...
Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai
IJCAI
1997
14 years 4 days ago
Learning to Improve both Efficiency and Quality of Planning
Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has b...
Tara A. Estlin, Raymond J. Mooney
ICML
2007
IEEE
14 years 11 months ago
Fast and effective kernels for relational learning from texts
In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such mode...
Alessandro Moschitti, Fabio Massimo Zanzotto