Rujirutana Srikanchana , Jianhua Xuan , Matthew T. Freedman , Charles C. ... Yue Wang , Tülay Adali , Chi-Ming Lau , Sun-Yuan Kung, Quantitative Analysis of MR Brain ...

Anil K. Jain , Robert P. W. Duin , Jianchang Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.1, p.4-37 ...

Streit, R. L. and Luginbuhl, T. E. (1994) Maximum likelihood training of probabilistic neural networks. IEEE Transactions on Neural Networks 5, 764--783.

In this we paper study the problem of combining the outputs of the members of an ensemble of neural networks. We review the commonly used methods and thoroughly derive a cost ...

TITLE= "Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model", JOURNAL= "Neural Computation",

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model J W Pillow, L Paninski, and E P Simoncelli Presented at: Neural Information Processing Systems (NIPS ...

ADAPTATION TO ENVIRONMENT AND SPEAKER USING MAXIMUM LIKELIHOOD NEURAL NETWORKS DongSuk YukJames Flanagan Mahesh Krishnamoorthy Krishna Dayanidhi CAIP Center, Rutgers University ...

Tsanakas}, title = {Mixture density estimation based on maximum likelihood and test statistics}, booktitle = {Neural Processing Letters}, year = {1999}, pages = {63--76}

Neural network parameters are estimated using the method of maximum likelihood instead of the back-propagation technique often used in the neural network literature.

Additional Information Citation: J. Grim, "Maximum-Likelihood Design of Layered Neural Networks ...

The authors present a method for training neural networks to minimize classification errors. The method is based on a maximum likelihood (ML) training algorithm. The ML criterion ...

J Sigma u j u u u ML OR MPL MAP NEURAL NETWORK Maximum likelihood neural network ML NN ffl Cost function Gamma log p tjx u ffl Optimize network with gradient descent ...

Recent work has examined the estimation of models of stimulus driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage.

Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage.

We examine a cascade encoding model for neural response in which a linear filtering stage is followed by a noisy, leaky, integrate-and-fire spike generation mechanism.

LETTER Communicated by Anthony Burkitt Maximum Likelihood Estimation ofa Stochastic Integrate-and-Fire Neural Encoding Model Liam Paninski liam@cns.nyu.edu Howard Hughes Medical ...

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model Liam Paninski. liam{at}cns.nyu.edu, Howard Hughes Medical Institute, Center for Neural ...

M. M. V. Hulle Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures Neural Comput., February 1, 2005; 18(2): 430 - 445.

Comparing ARTMAP neural network with Maximum-Likelihood for detecting urban change: the effect of class resolution: Journal Article

The aim of this paper is to present a neural network approach to crack location based on eddy-current back-scattering measured data inversion. A deep defect inside a conductive ...

Contributed article Robust maximum likelihood training ofheteroscedastic probabilistic neural networks Zheng RongYan g a, Sheng Chen b , * a Department of Electronics and Computer ...

Maximum Likelihood Estimation ofa Stochastic Integrate-and-Fire Neural Model JonathanW. Pillow, Liam Paninski, and Eero P. Simoncelli How ard Hughes Medical Institute Center for ...

Maximum Likelihood Estimation ofa Stochastic Integrate-and-Fire Neural Model JonathanW. Pillow, Liam Paninski, and Eero P. Simoncelli How ard Hughes Medical Institute Center for ...

Definition of Simulated Maximum Likelihood Estimation in the list of acronyms and abbreviations ... Simulated neural network: Simulated neural network Simulated Night Vertical Pinpoint ...

A MAXIMUM PARTIAL LIKELIHOOD FRAMEWORK FOR CHANNEL EQUALIZATION BY DISTRIBUTION LEARNING - Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop

Title: A Neural Network Assembly Memory Model with Maximum-Likelihood Recall and Recognition Properties Authors: Petro M. Gopych Categories: cs.AI Artificial Intelligence (cs.IR ...

Key Phrase page for maximum likelihood expression: ... Neural Networks for Pattern Recognition by Christopher M. ... Excerpt - on Page 45: " ... Thus, the ...

A Denial of Service Detector based on Maximum Likelihood Detection and the Random Neural Network Gülay Öke * and Georgios Loukas. Electrical and Electronic Engineering, Imperial ...

Maximum Likelihood Estimation Tutorial Slides by Andrew Moore. MLE is a solid tool for learning ... thing is that in order to understand things like polynomial regression, neural nets ...

Maximum-likelihood Estimation ... The neural network plays the role of a time-varying nonlinear observation on . An EKF can now be written to compute the maximum-likelihood estimate ...