Likelihood-based neural modeling: From networks to learning. Statistics for Biological Networks Workshop (2006). EURANDOM. The Netherlands. Analyzing neural connectivity using a ...
Likelihood-based neural data analysis: predicting and decoding spike trains; Estimation of information-theoretic quantities given limited data; Estimation and analysis of stochastic ...
Bias/Variance Decompositions for Likelihood-Based Estimators Neural Comp. 1998 10: 1425-1433. Isao Higuchi and Shinto Eguchi The Influence Function of Principal Component ...
Bias/Variance Decompositions for Likelihood-Based Estimators. Tom Heskes The bias/variance ... Neural Comput., May 1, 2004; 16(5): 1077 - 1104.
Likelihood-based modeling of neural responses . Jonathan W. Pillow . Published in: Bayesian Brain: Probabilistic Approaches to Neural Coding, eds. K Doya, S Ishii, A Pouget & R Rao ...
... Method, and the results are compared with those using the MVD Filter and the maximum-likelihood based SMLR Detector [8,9]. Compared with existing deconvolution methods, the Neural ...
Pillow JW. (2007) Likelihood-based approaches to modeling the neural code. In Bayesian Brain: Probabilistic Approaches to Neural Coding, eds. K Doya, S Ishii, A Pouget & R Rao.
Tom Heskes, Bias/variance decompositions for likelihood-based estimators, Neural Computation, v.10 n.6, p ... Doug W. Mahoney , Ruey-Pyng Lu , Shaun-inn Wu, Construction of an ...
... 3 conditional variance function - scientific articles matching the query: likelihood based ... Neural Model of a Grid-Based Map for Robot Sonar - Harris, Recce (1997) Neural ...
Matthew Partridge , Marwan Jabri, Hierarchical Feature Extraction for ... Tom Heskes, Bias/variance decompositions for likelihood-based estimators, Neural Computation, v.10 n.6, p ...
Bias-variance decompositions for likelihood-based estimators. Neural Computation, 10, 1425-1433. http://citeseer.comp.nus.edu.sg/70462.html More
... X) = the conditional mean of Y given X w = a vector of weights for a neural ... which, for example, could be the sum of squared errors or the negative log likelihood based on ...
An Introduction to Biological and Artificial Neural Networks for Pattern Recognition ... A.2 Log-Likelihood-Based Backpropagation Appendix B: Kolmogorov Results
... An Information-Theoretic Approach to Neural Computing ... Factorial Learning 208 Duality Between Unsupervised and Maximum Likelihood Based Supervised ...
... documents that cite the following paper: Bias/variance decompositions for likelihoodbased ... Hinton ? 2002 ? Neural Computation Cited by 139 (26 self) ...
Heskes T.M. Bias/variance decompositions for likelihood-based estimators. Neural Computation, vol. 10, pp. 1425-1433, 1998
Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Computation 10 (6): 1425-1433 (1998) 1997; 6 : Piërre van de Laar, Stan C. A. M.
Multiple neural spike train data analysis: state-of-the-art and future challenges ... to construct parametric models of conditional intensity functions and likelihood-based ...
A Neural Network For Classification With Incomplete Data - Morris (2000) .... likelihood based HMM system, using the same missing feature theory and the same method for detecting ...
Bishop (1995), an indispensable reference on neural networks, provides the following ... Do maximum likelihood using A. o Choose among the 3 nets trained by maximum likelihood based ...
Because the neural network model is nonlinear, the Kalman filter cannot be applied ... Maximum-likelihood based methods for estimating these quantities can be derived from the ...
Jesus Cortes: Jesus Cortes: Could synaptic depression be on the neural basis of visual ... 29th August: Stefan Harmeling: Likelihood-based estimators in the example of Robins-Ritov (joint ...
Track Neural Networks Free: Subscription: 549-025: Generalized Job-Shop ... 549-034: Likelihood based Classification in Bayesian Networks I. Stajduhar (Croatia) and I.
... we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural ...
... we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural ...
Maximum likelihood estimation of cascade point-process neural encoding models ... Finally, we note some interesting connections between the likelihood-based estimators and the ...
We will use likelihood based methods to build a consensus model of transcriptional regulation during neural development that explains our high -resolution mouse ...
A Neural Model of Contour Integration in the Primary Visual Cortex. 903-940 BibTeX ... Bias/Variance Decompositions for Likelihood-Based Estimators. 1425-1433 BibTeX; Isao Higuchi, ...
Image classification, Neural Network, Fuzzy theory, Minimum distance ... A., 1999, Likelihood-Based Segmentation and Classification of Remotely Sensed Images, Ph.D ...
... statistics for gaussian and non-gaussian data, regression, auto and cross correlations, reverse correlation, information theory and likelihood based model building for both neural ...
|
|