[引用][C] Clustering
R Xu - Wiley-IEEE Press google schola, 2008 - books.google.com
This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …
Color clustering and learning for image segmentation based on neural networks
G Dong, M Xie - IEEE transactions on neural networks, 2005 - ieeexplore.ieee.org
An image segmentation system is proposed for the segmentation of color image based on
neural networks. In order to measure the color difference properly, image colors are …
neural networks. In order to measure the color difference properly, image colors are …
A survey of hardware self-organizing maps
S Jovanović, H Hikawa - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Self-organizing feature maps (SOMs) are commonly used technique for clustering and data
dimensionality reduction in many application fields. Indeed, their inherent property of …
dimensionality reduction in many application fields. Indeed, their inherent property of …
SOMprocessor: A high throughput FPGA-based architecture for implementing Self-Organizing Maps and its application to video processing
MAA de Sousa, R Pires, E Del-Moral-Hernandez - Neural Networks, 2020 - Elsevier
The design of neuromorphic chips aims to develop electronic circuits dedicated to executing
artificial neural networks, mainly by exploring parallel processing. Unsupervised learning …
artificial neural networks, mainly by exploring parallel processing. Unsupervised learning …
Integer self-organizing maps for digital hardware
The Self-Organizing Map algorithm has been proven and demonstrated to be a useful
paradigm for unsupervised machine learning of two-dimensional projections of …
paradigm for unsupervised machine learning of two-dimensional projections of …
Improved learning performance of hardware self-organizing map using a novel neighborhood function
H Hikawa, Y Maeda - … on neural networks and learning systems, 2015 - ieeexplore.ieee.org
Many self-organizing maps (SOMs) implemented on hardware restrict their neighborhood
function values to negative powers of two. In this paper, we propose a novel hardware …
function values to negative powers of two. In this paper, we propose a novel hardware …
FPGA implementation of self organizing map with digital phase locked loops
H Hikawa - Neural Networks, 2005 - Elsevier
The self-organizing map (SOM) has found applicability in a wide range of application areas.
Recently new SOM hardware with phase modulated pulse signal and digital phase-locked …
Recently new SOM hardware with phase modulated pulse signal and digital phase-locked …
A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding
A Ramirez-Agundis, R Gadea-Girones… - Microprocessors and …, 2008 - Elsevier
This report describes the design of a modular, massive-parallel, neural-network (NN)-based
vector quantizer for real-time video coding. The NN is a self-organizing map (SOM) that …
vector quantizer for real-time video coding. The NN is a self-organizing map (SOM) that …
A reconfigurable neuroprocessor for self-organizing feature maps
J Lachmair, E Merenyi, M Porrmann, U Rückert - Neurocomputing, 2013 - Elsevier
In this paper we compare a scalable FPGA-based hardware accelerator for the emulation of
Self-Organizing Feature Maps (SOMs) with a multi-threaded software implementation on a …
Self-Organizing Feature Maps (SOMs) with a multi-threaded software implementation on a …
A scalable and adaptable hardware NoC-based self organizing map
Due to their ability to reduce the size of high-dimensional input data, self-organizing maps
(SOMs) can be employed as data quantizers. The widely used software implementations of …
(SOMs) can be employed as data quantizers. The widely used software implementations of …