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NECO
2008
111views more  NECO 2008»
15 years 6 months ago
A Neural Network Model of the Eriksen Task: Reduction, Analysis, and Data Fitting
We analyze a neural network model of the Eriksen task, a twoalternative forced choice task in which subjects must correctly identify a central stimulus and disregard flankers that...
Yuan Sophie Liu, Philip Holmes, Jonathan D. Cohen
CSL
2000
Springer
15 years 6 months ago
Detection of phonological features in continuous speech using neural networks
We report work on the first component of a two stage speech recognition architecture based on phonological features rather than phones. The paper reports experiments on three phon...
Simon King, Paul Taylor
CSB
2005
IEEE
202views Bioinformatics» more  CSB 2005»
16 years 8 days ago
Head and Neck Cancer Metastasis Prediction via Artificial Neural Networks
Cancers in head and neck tends to spread to nearby lymph nodes. Lymph nodes trap the spreading tumor cells but then the tumor starts to grow in these nodes and then spread further...
Eveleen Darby, Tezeswari Nettimi, Shilpa Kodali, L...
BMCBI
2006
146views more  BMCBI 2006»
15 years 6 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...
JMLR
2006
389views more  JMLR 2006»
15 years 6 months ago
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
Enrique Castillo, Bertha Guijarro-Berdiñas,...