Add to ORKGWe present a novel method based on a recently proposed extension to a negative feedback network which uses simple Hebbian learning to self-organise called Maximum Likelihood Hebbian learning [2]. We use the kernel version of the ML algorithm on data from a spectroscopic analysis of a stained glass rose window in a Spanish cathedral. It is hoped that in classifying the origin and date of each segment it will help in the restoration of this and other historical stain glass windows
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...
In this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for ...
Principal components analysis allows to reduce the dimensionality of a dataset in which there are la...
We investigate an extension of Hebbian learning in a principal component analysis network which has ...
In this paper, we review an extension of the learning rules in a Principal Component Analysis networ...
Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to fa...
We present a class of neural networks algorithms based on simple Hebbian learning which allow the fi...
The interdisciplinary research presented in this study is based on a novel approach to clustering ta...
A new method for performing a kernel principal component analysis is proposed. By kernelizing the ge...
A new method for performing a kernel principal component analysis is proposed. By kernelizing the ge...
This study introduces a novel fine-grained parallel implementation of a neural principal component a...
Abstract: This report consists of three chapters that together give a view of how the very simple st...
An important feature of neural networks is the ability they have to learn from their environment, an...
Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an ...
Principal component analysis based on Hebbian learning is originally designed for data processing in...
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...
In this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for ...
Principal components analysis allows to reduce the dimensionality of a dataset in which there are la...
We investigate an extension of Hebbian learning in a principal component analysis network which has ...
In this paper, we review an extension of the learning rules in a Principal Component Analysis networ...
Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to fa...
We present a class of neural networks algorithms based on simple Hebbian learning which allow the fi...
The interdisciplinary research presented in this study is based on a novel approach to clustering ta...
A new method for performing a kernel principal component analysis is proposed. By kernelizing the ge...
A new method for performing a kernel principal component analysis is proposed. By kernelizing the ge...
This study introduces a novel fine-grained parallel implementation of a neural principal component a...
Abstract: This report consists of three chapters that together give a view of how the very simple st...
An important feature of neural networks is the ability they have to learn from their environment, an...
Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an ...
Principal component analysis based on Hebbian learning is originally designed for data processing in...
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...
In this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for ...
Principal components analysis allows to reduce the dimensionality of a dataset in which there are la...