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» Regularized Learning with Networks of Features
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VLSISP
2002
114views more  VLSISP 2002»
14 years 11 months ago
Image processing using cellular neural networks based on multi-valued and universal binary neurons
Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an a...
Igor N. Aizenberg, Constantine Butakoff
CORR
2010
Springer
149views Education» more  CORR 2010»
14 years 12 months ago
Using Rough Set and Support Vector Machine for Network Intrusion Detection
The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a ...
Rung Ching Chen, Kai-Fan Cheng, Chia-Fen Hsieh
AAAI
2008
15 years 2 months ago
RADAR: A Personal Assistant that Learns to Reduce Email Overload
Email client software is widely used for personal task management, a purpose for which it was not designed and is poorly suited. Past attempts to remedy the problem have focused o...
Michael Freed, Jaime G. Carbonell, Geoffrey J. Gor...
CIKM
2010
Springer
14 years 10 months ago
Discriminative factored prior models for personalized content-based recommendation
Most existing content-based filtering approaches including Rocchio, Language Models, SVM, Logistic Regression, Neural Networks, etc. learn user profiles independently without ca...
Lanbo Zhang, Yi Zhang 0001
CCS
2007
ACM
15 years 6 months ago
Network anomaly detection based on TCM-KNN algorithm
Intrusion detection is a critical component of secure information systems. Network anomaly detection has been an active and difficult research topic in the field of Intrusion Dete...
Yang Li, Binxing Fang, Li Guo, You Chen