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NECO
1998
171views more  NECO 1998»
8 years 6 months ago
Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization
three different levels of abstraction: detailed models including ctivity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, an...
Laurenz Wiskott, Terrence J. Sejnowski
NECO
1998
73views more  NECO 1998»
8 years 6 months ago
Chaotic Balanced State in a Model Of Cortical Circuits
Carl van Vreeswijk, Haim Sompolinsky
NECO
1998
119views more  NECO 1998»
8 years 6 months ago
Density Estimation by Mixture Models with Smoothing Priors
In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the ce...
Akio Utsugi
NECO
1998
121views more  NECO 1998»
8 years 6 months ago
Nonlinear Time-Series Prediction with Missing and Noisy Data
We derive solutions for the problem of missing and noisy data in nonlinear timeseries prediction from a probabilistic point of view. We discuss different approximations to the so...
Volker Tresp, Reimar Hofmann
NECO
1998
168views more  NECO 1998»
8 years 6 months ago
Constructive Incremental Learning from Only Local Information
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, t...
Stefan Schaal, Christopher G. Atkeson
NECO
1998
69views more  NECO 1998»
8 years 6 months ago
The Role of the Hippocampus in Solving the Morris Water Maze
A. David Redish, David S. Touretzky
NECO
1998
151views more  NECO 1998»
8 years 6 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...
NECO
1998
83views more  NECO 1998»
8 years 6 months ago
Properties of Support Vector Machines
Support Vector Machines (SVMs) perform pattern recognition between two point classes by nding a decision surface determined by certain points of the training set, termed Support V...
Massimiliano Pontil, Alessandro Verri
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