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IJCNN
2006
IEEE
14 years 2 months ago
Adaptation of Artificial Neural Networks Avoiding Catastrophic Forgetting
— In connectionist learning, one relevant problem is “catastrophic forgetting” that may occur when a network, trained with a large set of patterns, has to learn new input pat...
Dario Albesano, Roberto Gemello, Pietro Laface, Fr...
CORR
2010
Springer
209views Education» more  CORR 2010»
13 years 9 months ago
An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems
Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict bett...
S. M. Kamruzzaman, Md. Monirul Islam
BMCBI
2004
133views more  BMCBI 2004»
13 years 8 months ago
Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction m
Background: Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have pre...
Yasuyuki Tomita, Shuta Tomida, Yuko Hasegawa, Yoic...
BMCBI
2010
118views more  BMCBI 2010»
13 years 9 months ago
Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry
Background: There is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometr...
Bing Wang, Steve Valentine, Manolo Plasencia, Srir...
BMCBI
2007
162views more  BMCBI 2007»
13 years 9 months ago
Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis
Background: Genome-wide identification of specific oligonucleotides (oligos) is a computationallyintensive task and is a requirement for designing microarray probes, primers, and ...
Chun-Chi Liu, Chin-Chung Lin, Ker-Chau Li, Wen-Shy...