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» Training Methods for Adaptive Boosting of Neural Networks
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113
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FLAIRS
2004
15 years 2 months ago
A Method Based on RBF-DDA Neural Networks for Improving Novelty Detection in Time Series
Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this...
Adriano L. I. Oliveira, Fernando Buarque de Lima N...
137
Voted
BMCBI
2010
149views more  BMCBI 2010»
15 years 21 days ago
A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context
Background: Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex eti...
Ioannis K. Valavanis, Stavroula G. Mougiakakou, Ke...
ICDAR
2003
IEEE
15 years 5 months ago
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods th...
Patrice Simard, David Steinkraus, John C. Platt
131
Voted
ICASSP
2007
IEEE
15 years 7 months ago
Integrating Relevance Feedback in Boosting for Content-Based Image Retrieval
Many content-based image retrieval applications suffer from small sample set and high dimensionality problems. Relevance feedback is often used to alleviate those problems. In thi...
Jie Yu, Yijuan Lu, Yuning Xu, Nicu Sebe, Qi Tian
88
Voted
IJCAI
1997
15 years 1 months ago
An Effective Learning Method for Max-Min Neural Networks
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ m...
Loo-Nin Teow, Kia-Fock Loe