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» Training Methods for Adaptive Boosting of Neural Networks
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CNSR
2004
IEEE
174views Communications» more  CNSR 2004»
15 years 1 months ago
Network Intrusion Detection Using an Improved Competitive Learning Neural Network
This paper presents a novel approach for detecting network intrusions based on a competitive learning neural network. In the paper, the performance of this approach is compared to...
John Zhong Lei, Ali A. Ghorbani
IJPRAI
1998
100views more  IJPRAI 1998»
14 years 9 months ago
Obtaining The Correspondence between Bayesian and Neural Networks
We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we c...
Athena Stassopoulou, Maria Petrou
CAMAD
2006
IEEE
15 years 1 months ago
P2PRealm - peer-to-peer network simulator
Abstract--Peer-to-Peer Realm (P2PRealm) is an efficient peer-topeer network simulator for studying algorithms based on neural networks. In contrast to many simulators, which emphas...
Niko Kotilainen, Mikko Vapa, Teemu Keltanen, Annem...
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 3 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
NLPRS
2001
Springer
15 years 2 months ago
Named Entity Recognition using Machine Learning Methods and Pattern-Selection Rules
Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and QuestionAnswering system. This paper propos...
Choong-Nyoung Seon, Youngjoong Ko, Jeong-Seok Kim,...