نتایج جستجو برای: organizing map som neural networks finally

تعداد نتایج: 1198270  

2004
Marc Strickert

This work investigates the self-organizing representation of temporal data in prototypebased neural networks. Extensions of the supervised learning vector quantization (LVQ) and the unsupervised self-organizing map (SOM) are considered in detail. The principle of Hebbian learning through prototypes yields compact data models that can be easily interpreted by similarity reasoning. In order to ob...

Journal: :CoRR 2016
Jyothi Korra

This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using...

2005
John Hammond Stephen Fischer Iren Valova

The self-organizing map (SOM) is a common methodology used to capture and represent data patterns and increasingly playing a significant role in the development of neural networks. The primary objective of an SOM is to determine an approximate representation of data with an unknown probability distribution, from a multi-dimensional input space, using a lower dimensional neural network. The appr...

2003
Jung Hwan Kim Byung Ro Moon

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantizations, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve mo...

2011
VASANTHA KUMARI

The method of self-organizing maps (SOM) is a method of exploratory data analysis used for clustering and projecting multi-dimensional data into a lower-dimensional space to reveal hidden structure of the data. The Self-Organizing Feature Maps (SOFMs) [11] is a class of neural networks capable of recognizing the main features of the data they are trained on. There is extensive literature on its...

Journal: :Hydrology Research 2022

Abstract The current research attempts to present a modeling framework for determining soil moisture conditions by using remotely sensed imagery products. In this way, identifying various pixels with similar patterns from satellite images could be reliable method have an appropriate view over the condition of particular region. context, artificial intelligence-based self-organizing map (SOM) is...

2004
Alireza Fatehi Kenichi Abe

The identification method of multiple modeling by the irregular self-organizing map (MMISOM) neural network is presented, which improves the authors’ previous method of MMSOM that uses the rectangular SOM. Inputs to the neural networks are parameters of the instantaneous model computed adaptively in each instant of time. The reference vectors of its output nodes are the parameters estimation of...

Journal: :Neural networks : the official journal of the International Neural Network Society 2002
Eric de Bodt Marie Cottrell Michel Verleysen

Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the...

2017
Krishna Kumar Krishan Kumar Rahul Mishra

Motion estimation is a very important and interesting area of research. It has become the necessity of many fields such as agriculture, security, medicine, traffic, and sports, the growth of a plant, tracking the movement of a vehicle within traffic, or observing the movements of a runner's hands or legs. Traditional methods for motion estimation estimate the motion field between a pair of imag...

2017
Spyridon Revithis

Artificial General Intelligence (AGI) is a term that describes a variant of a Strong AI revival in the mind sciences. Irrespective of its definition limits, and leaving aside the non-scientific metaphysical or philosophical aspirations, AGI studies the feasibility and implementation aspects of artificial systems that would have the capacity of domain non-specific (domaingeneral) human-level int...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید