. In this article, we review unsupervised neural network learning procedures which can be applied to the task of preprocessing raw data to extract useful features for subsequent classification. The learning algorithms reviewed here are grouped into three sections: information-preserving methods, density estimation methods, and feature extraction methods. Each of these major sections concludes with a discussion of successful applications of the methods to real-world problems. Keywords: Unsupervised learning, self-organization, information theory, feature extraction, signal processing 1. Introduction One of the more difficult and important parts of the classification process is the preprocessing of raw data to extract useful and appropria...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
Absfract- Networks of linear units are the simplest kind of networks, where the basic questions rela...
Research on image classification has grown rapidly in the field of machine learning. Many methods ha...
The motivation for this dissertation is two-prong. Firstly, the current state of machine learning im...
AbstractIn this article, we consider unsupervised learning from the point of view of applying neural...
The paper suggests a statistical framework for the parameter esti-mation problem associated with uns...
This article presents a review of traditional and current methods of classification in the framework...
The recent rise in machine learning has been largely made possible by novel algorithms, such as con...
Algorithms for object extraction using a neural network are proposed. A single neuron (processor) is...
Thesis (Ph.D.)--University of Washington, 2020Unsupervised learning is the branch of machine learnin...
For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object d...
Feature extraction is the heart of an object recognition system. In recognition problem, features ar...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
Neural networks have been successfully applied in a wide range of supervised and unsupervised learni...
To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
Absfract- Networks of linear units are the simplest kind of networks, where the basic questions rela...
Research on image classification has grown rapidly in the field of machine learning. Many methods ha...
The motivation for this dissertation is two-prong. Firstly, the current state of machine learning im...
AbstractIn this article, we consider unsupervised learning from the point of view of applying neural...
The paper suggests a statistical framework for the parameter esti-mation problem associated with uns...
This article presents a review of traditional and current methods of classification in the framework...
The recent rise in machine learning has been largely made possible by novel algorithms, such as con...
Algorithms for object extraction using a neural network are proposed. A single neuron (processor) is...
Thesis (Ph.D.)--University of Washington, 2020Unsupervised learning is the branch of machine learnin...
For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object d...
Feature extraction is the heart of an object recognition system. In recognition problem, features ar...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
Neural networks have been successfully applied in a wide range of supervised and unsupervised learni...
To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that...
A novel neural network based method for feature extraction is proposed. The method achieves dimensio...
Absfract- Networks of linear units are the simplest kind of networks, where the basic questions rela...
Research on image classification has grown rapidly in the field of machine learning. Many methods ha...