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» Introduction to artificial neural networks
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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 8 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
IWANN
2009
Springer
14 years 2 months ago
A Genetic Algorithm for ANN Design, Training and Simplification
This paper proposes a new evolutionary method for generating ANNs. In this method, a simple real-number string is used to codify both architecture and weights of the networks. Ther...
Daniel Rivero, Julian Dorado, Enrique Ferná...
IJCNN
2008
IEEE
14 years 2 months ago
Robust modular ARTMAP for multi-class shape recognition
— This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP). The prediction...
Chue Poh Tan, Chen Change Loy, Weng-Kin Lai, Chee ...
DSD
2007
IEEE
150views Hardware» more  DSD 2007»
14 years 2 months ago
Adaptive Distance Estimation and Localization in WSN using RSSI Measures
Abstract—Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper,...
Abdalkarim Awad, Thorsten Frunzke, Falko Dressler
JCIT
2010
156views more  JCIT 2010»
13 years 2 months ago
Intelligent Monitoring Approach for Pipeline Defect Detection from MFL Inspection
Artificial Neural Networks(ANNS) have top level of capability to progress the estimation of cracks in metal tubes. The aim of this paper is to propose an algorithm to identify mod...
Saeedreza Ehteram, Seyed Zeinolabedin Moussavi, Mo...