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» Bagging and Boosting Negatively Correlated Neural Networks
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TKDE
2008
114views more  TKDE 2008»
13 years 5 months ago
Neural-Based Learning Classifier Systems
UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classificati...
Hai Huong Dam, Hussein A. Abbass, Chris Lokan, Xin...
ESANN
2006
13 years 7 months ago
Diversity creation in local search for the evolution of neural network ensembles
Abstract. The EENCL algorithm [1] automatically designs neural network ensembles for classification, combining global evolution with local search based on gradient descent. Two mec...
Pete Duell, Iris Fermin, Xin Yao
ICML
2003
IEEE
14 years 6 months ago
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods
We analyze the formal grounding behind Negative Correlation (NC) Learning, an ensemble learning technique developed in the evolutionary computation literature. We show that by rem...
Gavin Brown, Jeremy L. Wyatt
ICONIP
2008
13 years 7 months ago
An Evaluation of Machine Learning-Based Methods for Detection of Phishing Sites
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...
Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobaya...
ISNN
2005
Springer
13 years 11 months ago
Internet Traffic Prediction by W-Boost: Classification and Regression
Abstract. Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic m...
Hanghang Tong, Chongrong Li, Jingrui He, Yang Chen