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ISNN
2005
Springer
14 years 1 months ago
Non-parametric Statistical Tests for Informative Gene Selection
This paper presents two non-parametric statistical test methods, called Kolmogorov-Smirnov (KS) and U statistic test methods, respectively, for informative gene selection of a tumo...
Jinwen Ma, Fuhai Li, Jianfeng Liu
GECCO
2009
Springer
128views Optimization» more  GECCO 2009»
14 years 2 months ago
Neural network ensembles for time series forecasting
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Victor M. Landassuri-Moreno, John A. Bullinaria
PRIS
2004
13 years 9 months ago
Neural Network Learning: Testing Bounds on Sample Complexity
Several authors have theoretically determined distribution-free bounds on sample complexity. Formulas based on several learning paradigms have been presented. However, little is kn...
Joaquim Marques de Sá, Fernando Sereno, Lu&...
APIN
2004
116views more  APIN 2004»
13 years 7 months ago
Neural Learning from Unbalanced Data
This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three differen...
Yi Lu Murphey, Hong Guo, Lee A. Feldkamp
NIPS
2007
13 years 9 months ago
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smooth...
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy...