Self Organized Map Research Papers (original) (raw)

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  • Algorithms, Artificial Intelligence, Telecommunications, Cell Phones
• • by horst langer
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  • Geology, Geochemistry, Geophysics, Volcanology

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps... more

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied ...

• • by Jari Kangas
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  • Self Organized Map, Indexation, Literature survey, COL
• • by David West
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  • Neural Network, Multidisciplinary, Unsupervised Learning, Cluster Analysis
• • by Liviu Vladutu
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  • Bioinformatics, Artificial Intelligence, Social Networks, Machine Learning

Clustering geographically referenced data is an important issue in Geographic Information Science. There are several algorithms that can be used in such tasks, the self- organizing map (SOM) is one of the most popular tools in this... more

Clustering geographically referenced data is an important issue in Geographic Information Science. There are several algorithms that can be used in such tasks, the self- organizing map (SOM) is one of the most popular tools in this context. Although the standard SOM can be used geographically referenced data, it is useful to have a clustering tool that takes into account

• • by V. Lobo
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  • Geographic Information Science, Spatial Data, Self Organized Map, Geographic Information System
• • by DARIO JARA
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  • Haptic Perception, Self Organization, Self Organized Map
• • by Jan Faigl
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  • Engineering, Neural Network, Path planning, Traveling Salesman Problem
• • by Miklas Scholz
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  • Water, Wetlands, Water quality, Modeling
• • by Bojan Soptrajanov
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  • Genetic Algorithms, Genetic Algorithm, Infrared spectroscopy, Classification
• • by Jorma Laaksonen
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  • Artificial Intelligence, Neural Networks, Multidisciplinary, Entropy
• • by Shazia Afzal
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  • Computer Science, Inductive Learning, Self Organized Map, TSD
• • by Liviu Vladutu
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  • Computer Networks, Radial Basis Function, Process Control, Support Vector Machines

Our ability to demonstrate statistical patterns of invasion by non-native species will determine the success of future management projects. We investigated the suitability of self-organizing maps (SOM, neural network) for patterning... more

Our ability to demonstrate statistical patterns of invasion by non-native species will determine the success of future management projects. We investigated the suitability of self-organizing maps (SOM, neural network) for patterning habitat invasion by exotic fish species at the regional scale (Southwest France), using a binary dataset of species occurrences. The SOM visualization can be used as an analytical tool to bring out relationships between sample locations and biological variables, but in addition the weight of each species in the output of the SOM can be interpreted as its occurrence probability in various geographic areas. After training the SOM with fish presence/absence data, the k-means algorithm helped to derive three major clusters of sites (headwater, montane, and plain areas). Each cluster was divided into two subsets of sites according to non-native fish, because assemblage compositions delineated different geological areas: Pyrenees Mountains, Massif Central Moun...

• • by SYLVAIN MASTRORILLO
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  • Neural Networks, Neural Network, Conservation planning, Biological Sciences
• • by Emilio Soria and +2
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  • Marketing, Reinforcement Learning, Neural Networks, Neural Network
• • by Dieter Merkl
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  • Functional Analysis, Data Mining, Data Analysis, Pattern Recognition
• • by Tatiana von Landesberger
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  • Visualization, Information Visualization, Data Analysis, Principal Component Analysis
• • by Jing-jia Luo
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  • Oceanography, Climate variability, East Africa, Indian Ocean

This paper presents a complementary metal-oxide-semiconductor (CMOS) implementation of a conscience mechanism used to improve the effectiveness of learning in the winner-takes-all (WTA) artificial neural networks (ANNs) realized at the... more

This paper presents a complementary metal-oxide-semiconductor (CMOS) implementation of a conscience mechanism used to improve the effectiveness of learning in the winner-takes-all (WTA) artificial neural networks (ANNs) realized at the transistor level. This mechanism makes it possible to eliminate the effect of the so-called ¿dead neurons,¿ which do not take part in the learning phase competition. These neurons usually have a detrimental effect on the network performance, increasing the quantization error. The proposed mechanism comes as part of the analog implementation of the WTA neural networks (NNs) designed for applications to ultralow power portable diagnostic devices for online analysis of ECG biomedical signals. The study presents Matlab simulations of the network's model, discusses postlayout circuit level simulations and includes results of measurement completed for the physical realization of the circuit.

• • by Tomasz Talaska
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  • Game Theory, Analog Circuits, Neural Networks, Conscience
• • by Fatma Kandemirli
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  • Organic Chemistry, Medicinal Chemistry, Tuberculosis and Infectious Disease, Neural Network

Abstract: The current data tends to be more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a smaller data in volume and has the same analytical results as the... more

Abstract: The current data tends to be more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a smaller data in volume and has the same analytical results as the original representation. A clustering process needs data reduction to obtain an efficient processing time while clustering and mitigate curse of dimensionality. This paper proposes a model for extracting multidimensional data clustering of health database. We implemented four dimension ...

• • by abdullah embong
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  • Principal Component Analysis, Dimension Reduction, Information, Singular value decomposition

In this research, a special form of Automated Guided Vehicle (AGV) routing problem is investigated. The objective is to find the shortest tour for a single, free-ranging AGV that has to carry out multiple pick and deliver (P&D) requests.... more

In this research, a special form of Automated Guided Vehicle (AGV) routing problem is investigated. The objective is to find the shortest tour for a single, free-ranging AGV that has to carry out multiple pick and deliver (P&D) requests. This problem is an incidence of the asymmetric traveling salesman problem which is known to be NP-complete. An artificial neural network algorithm based on Kohonen's self-organizing feature maps is developed to solve the problem, and several improvements on the basic features of self-organizing maps are proposed. Performance of the algorithm is tested under various parameter settings for different P&D request patterns and problem sizes, and compared with the optimal solution and the nearest neighbor rule. Promising results are obtained in terms of solution quality and computation time.

• • by Sinan Kayaligil
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  • Neural Network, Multidisciplinary, Nearest Neighbor, Self Organization
• • by Dina Goren-bar
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  • Supervised Learning, Self Organized Map, Automatic Classification
• • by Tarek Gharib
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  • Document Clustering, Knowledge base, Self Organized Map
• • by Ashu Jain
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  • Information Systems, Applied Mathematics, Back Propagation, Artificial Neural Networks
• • by A.T. Marques
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  • Engineering, Pattern Recognition, Composite Materials, Damage detection
• • by Dr. Minakshi Gogoi
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  • Data Mining, Pattern Recognition, Neural Network, Hausdorff Distance
• • by Rewbenio A . Frota
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  • Cognitive Science, Data Mining, Pattern Recognition, Radio Resource Management
• • by Eduardo Mercado
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  • Psychology, Cognitive Science, Algorithms, Neural Network

This paper presents an effective method for fingerprint verification based on a data mining technique called minutiae clustering and a graph-theoretic approach to analyze the process of fingerprint comparison to give a feature space... more

This paper presents an effective method for fingerprint verification based on a data mining technique called minutiae clustering and a graph-theoretic approach to analyze the process of fingerprint comparison to give a feature space representation of minutiae and to produce a lower bound on the number of detectably distinct fingerprints. The method also proving the invariance of each individual fingerprint by using both the topological behavior of the minutiae graph and also using a distance measure called Hausdorff distance.The method provides a graph based index generation mechanism of fingerprint biometric data. The self-organizing map neural network is also used for classifying the fingerprints.

• • by Dr. Minakshi Gogoi
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  • Data Mining, Pattern Recognition, Neural Network, Hausdorff Distance

A new classification method, for isolating steam generator tube defects in nuclear power plants using Eddy Current Test (ECT) signals, has been developed. The method uses Self-Organizing maps (SOM) with different data signatures to... more

A new classification method, for isolating steam generator tube defects in nuclear power plants using Eddy Current Test (ECT) signals, has been developed. The method uses Self-Organizing maps (SOM) with different data signatures to identify and classify these defects. A multiple inference system is proposed which evaluates different extracted characteristic SOMs to infer the defect type. Wavelet zero-crossing representation, a linear predictive coding (LPC), and other basic signal representations, such as magnitude and phase, are used to construct characteristic vectors that combine one or more of these features. These vectors are evaluated for their ability to classify tube defects and the ones with the best performance are used in the multiple inference system. The effectiveness of the method is demonstrated by applications of the characteristic maps to ECT data from various cases of tube defects in pressurized water reactor plant steam generators. The developed algorithm enables real-time applications such as fast tube defects classification systems and visualization of ECT signal feature prototypes, which may improve the speed of time-critical decision making during power plant maintenance outages.

• • by eduardo L L cabral
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  • Distributed Computing, Decision Making, Power Plant, Computer Software

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM... more

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer

• • by Weng Ah
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  • Computational Complexity, Pattern Recognition, Face Recognition, Access Control
• • by Jari Kangas
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  • High Dimensionality, Self Organized Map
• • by Marcilio Matos
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  • Geophysics, Algorithms, Pattern Recognition, Interpretation
• • by Tadahiko Murata
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  • Engineering, Genetics, Evolutionary Computation, Genetic Drift
• • by Arnold Baca
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  • Engineering, Computer Graphics, Motor Control, Human Movement Science
• • by atif mustafa
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  • Earth Sciences, Wetlands, Water quality, Principal Component Analysis
• • by Raúl Medina and +1
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  • Civil Engineering, Geology, Coastal Engineering, Data Mining
• • by Miguel Verdugo
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  • Quality of life, Quality Improvement, Intellectual Disability, Scale Development
• • by Azween Abdullah
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  • Computer Science, Neural Network, Network Intrusion Detection & Prevention, Intrusion Detection
• • by François Delmas
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  • Water quality, Principal Component Analysis, Brackish Water, Neural Network
• • by Diego Evin
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  • Support vector machine, Self Organized Map, Palabras Clave: BIM
• • by Aini Hussain
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  • Engineering, Computer Vision, Pattern Recognition, Image Analysis

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps... more

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied ...

• • by J. Kangas
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  • Self Organized Map, Indexation, Literature survey, COL
• • by Yan Ivanenkov
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  • Computational Biology, Support Vector Machines, Drug Discovery, Pharmacokinetics
• • by Leandro Silva
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  • Statistical Analysis, Image Classification, Human Performance, Self Organized Map
• • by Kyoko Kanzaki
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  • Self Organization, Self Organized Map
• • by Julian W. Gardner
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  • Engineering, Principal Component Analysis, Neural Networks, Neural Network
• • by Jay Lee
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  • Engineering, Performance Assessment, Low Frequency, Predictive Maintenance

In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse... more

In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.

• • by Murat Topallar
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  • Anomaly Detection, Decision support system, Decision Trees, Computer Network