A Novel Kohonen Self-organizing Maps Using Exponential Decay Average Rate of Change for Color Clustering

2019 ◽  
pp. 23-33 ◽  
Author(s):  
Edwin F. Galutira ◽  
Arnel C. Fajardo ◽  
Ruji P. Medina
Keyword(s):  
Exponential Decay ◽  
Average Rate ◽  
Rate Of Change ◽  
Self Organizing Maps ◽  
Color Clustering ◽  
Self Organizing

scholarly journals Antarctic sea ice: a self-organizing map-based perspective

2007 ◽  
Vol 46 ◽  
pp. 391-396 ◽  
Author(s):  
David B. Reusch ◽  
Richard B. Alley
Keyword(s):  
Sea Ice ◽  
Rapid Identification ◽  
Rate Of Change ◽  
Spatial And Temporal Variability ◽  
Self Organizing Map ◽  
Sea Ice Extent ◽  
Self Organizing Maps ◽  
Antarctic Sea Ice ◽  
Sea Ice Edge ◽  
Self Organizing

AbstractSelf-organizing maps (SOMs) provide a powerful, non-linear technique to optimally summarize a complex geophysical dataset using a user-selected number of ‘icons’ or SOM states, allowing rapid identification of preferred patterns, predictability of transitions, rates of transitions, and hysteresis in cycles. The use of SOMs is demonstrated here through application to a 24 year dataset (1973–96) of monthly Antarctic sea-ice edge positions. Variability in sea-ice extent, concentration and other physical characteristics is an important component of the Earth’s dynamic climate system, particularly in the Southern Hemisphere where annual changes in sea-ice extent (temporarily) double the size of the Antarctic cryosphere. SOM-based patterns concisely capture the spatial and temporal variability in these data, including the annual progression of expansion and retreat, a general eastward propagation of anomalies during the winter, and sub-annual variability in the rate of change in extent at different times of the year (e.g. retreat in January is faster than in November). There is also often a general seasonal hysteresis, i.e. monthly anomalies during cooling follow a different spatial path than during warming.

scholarly journals ANALYSIS OF THE SELF-ORGANIZING MAP-BASED INVESTMENT STRATEGY

2017 ◽  
pp. 10-17
Author(s):  
Piotr Kossakowski ◽  
Piotr Bilski
Keyword(s):  
Average Rate ◽  
Investment Strategy ◽  
Correlated Data ◽  
Self Organizing Map ◽  
Training Set ◽  
Self Organizing Maps ◽  
Strategy Performance ◽  
The Impact ◽  
Process Method ◽  
Self Organizing

The following paper presents the application of Self-Organizing Maps (SOM) to construct and apply investment strategy on the stock market. Characteristics of this type of neural network and their influence on the investment strategy performance are verified. Considered parameters include the SOM size, here connected to the size of the training set (number of examples). The average number of patterns per neuron was selected as the appropriate measure. Other aspects of the SOM analysis included conscience mechanism, which allows more neurons to be stimulated during the learning process, method of weights updating, determining the number of stimulated neurons. Additionally, the impact of the correlation between features was verified to eliminate redundant ones. Performance of each designed network was verified against the simple investment strategy, generating “buy”, ”sell” and “hold” signals based on the average Rate of Return (RoR). Results show that SOMs with the conscience mechanism outperform their simpler configurations. Elimination of correlated data also improves performance of the SOM-based investment strategy.

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity

2021 ◽  
Vol 11 (11) ◽  
pp. 5072
Author(s):  
Byung-Kook Koo ◽  
Ji-Won Baek ◽  
Kyung-Yong Chung
Keyword(s):  
Traffic Accidents ◽  
Average Rate ◽  
Modern Society ◽  
Rate Of Change ◽  
Weighted Voting ◽  
Independent Variables ◽  
Dependent Variables ◽  
Accident Severity ◽  
Harmonic Average ◽  
Bagging Method

Traffic accidents are emerging as a serious social problem in modern society but if the severity of an accident is quickly grasped, countermeasures can be organized efficiently. To solve this problem, the method proposed in this paper derives the MDG (Mean Decrease Gini) coefficient between variables to assess the severity of traffic accidents. Single models are designed to use coefficient, independent variables to determine and predict accident severity. The generated single models are fused using a weighted-voting-based bagging method ensemble to consider various characteristics and avoid overfitting. The variables used for predicting accidents are classified as dependent or independent and the variables that affect the severity of traffic accidents are predicted using the characteristics of causal relationships. Independent variables are classified as categorical and numerical variables. For this reason, a problem arises when the variation among dependent variables is imbalanced. Therefore, a harmonic average is applied to the weights to maintain the variables’ balance and determine the average rate of change. Through this, it is possible to establish objective criteria for determining the severity of traffic accidents, thereby improving reliability.

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

2021 ◽  
pp. 107410
Author(s):  
I. Álvarez ◽  
J.S. Font-Muñoz ◽  
I. Hernández-Carrasco ◽  
C. Díaz-Gil ◽  
P.M. Salgado-Hernanz ◽  
...  
Keyword(s):  
Fish Assemblage ◽  
Larval Fish ◽  
Self Organizing Maps ◽  
Self Organizing
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