Self-organizing yprel network population for distributed classification problem solving

1999 ◽  
pp. 843-852
Author(s):  
Emmanuel Stocker ◽  
Arnaud Ribert ◽  
Yves Lecourtier
Keyword(s):  
Problem Solving ◽  
Classification Problem ◽  
Distributed Classification ◽  
Self Organizing

Self-Organizing Map Application for Iris Recognition

2014 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Anna Sedrak Hovakimyan ◽  
Siranush Gegham Sargsyan ◽  
Arshak Nazaryan
Keyword(s):  
Pattern Recognition ◽  
Problem Solving ◽  
Iris Recognition ◽  
Classification Problem ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
Human Iris ◽  
Iris Patterns ◽  
Self Organizing

Human iris is  a good subject of biometrical identification, since  iris patterns are unique like fingerprints. Iris is well protected against damage, unlike fingerprints, which can be harder to recognize after years of certain types of manual labor.A problem of iris recognition is considered in the paper. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern classification is an example of pattern recognition: it attempts to assign each input value to one of a given set of classes. Nowadays various techniques are used for this purpose, and in particular artificial neural networks.For iris recognition problem solving  Kohenen Self Organizing Maps are suggested to use. The software for iris recognition is developed  which is customizable and allows to select the appropriate parameters of the neural network to obtain the most satisfactory results. The developed Self-Organizing Map Library of classes can be used for various kinds of object classification problem solving as well as for any problems suitable to solve with Self-Organizing Maps.

Fuzzy Control as Self-Organizing Adaptive Constraint-Oriented Problem Solving

1995 ◽  
pp. 111-122 ◽  
Author(s):  
Osamu Katai ◽  
Masaaki Ida ◽  
Tetsuo Sawaragi ◽  
Kiminori Shimamoto ◽  
Sosuke Iwai ◽  
...  
Keyword(s):  
Problem Solving ◽  
Fuzzy Control ◽  
Self Organizing

scholarly journals TECHNIQUE OF THINKING STYLE EVALUATING

2015 ◽  
Vol 3 (2) ◽  
pp. 1-8
Author(s):  
Alla Belousova ◽  
Vlada Pishchik
Keyword(s):  
Problem Solving ◽  
Representative Sample ◽  
Reliability And Validity ◽  
Thinking Styles ◽  
Psychometric Analysis ◽  
Thinking Style ◽  
Characteristic Set ◽  
Information Functions ◽  
And Function ◽  
Self Organizing

The results of psychometric analysis of the new technique of thinking styles diagnostics are presented. The fundamental principles of thinking style concept by A. Belousova, according to which the thinking style is determined by the dominance of a person’s function in the structure of thinking activity during the problem solving, are covered. In accordance with A. Belousova’s ideas that the collaborative thinking activity as a self-organizing system is carried out by means of functions assumed by each participant: function of generating ideas, the function of selection (review and evaluation of information), functions of sense transfer and function of implementation. Thinking of adult, acting as a complex self-organizing system, combines the same functions: generation, selection, sense transfer and implementation. In this connection, we believe that the thinking style is defined as a characteristic set of functions actualized by a person in different situations of the problem solving. Domination of generation function determines the development of initiative thinking style, selection - critical, sense transfer - administrative, implementation - practical. The results of testing the reliability and validity of a new questionnaire for the thinking style diagnostics on a representative sample of Russians are given. The author’s version of the questionnaire is presented.

scholarly journals Mining EEG with SVM for Understanding Cognitive Underpinnings of Math Problem Solving Strategies

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Paul Bosch ◽  
Mauricio Herrera ◽  
Julio López ◽  
Sebastián Maldonado
Keyword(s):  
Problem Solving ◽  
Phase Synchronization ◽  
Binary Classification ◽  
Electric Activity ◽  
Classification Problem ◽  
Network Size ◽  
Machine Learning Techniques ◽  
Math Problem ◽  
Math Problem Solving ◽  
Problem Solving Strategies

We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.

scholarly journals Spiking Self-organizing Maps for Classification Problem

2013 ◽  
Vol 11 ◽  
pp. 57-64 ◽  
Author(s):  
Bariah Yusob ◽  
Siti Mariyam Hj Shamsuddin ◽  
Haza Nuzly Abdull Hamed
Keyword(s):  
Classification Problem ◽  
Self Organizing Maps ◽  
Self Organizing

Using Self-Organizing Map and Clustering to Investigate Problem-Solving Patterns in the Massive Open Online Course: An Exploratory Study

2018 ◽  
Vol 57 (2) ◽  
pp. 471-490 ◽  
Author(s):  
Youngjin Lee
Keyword(s):  
Problem Solving ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Online Course ◽  
Analysis Tool ◽  
Self Organizing Map ◽  
Multiple Perspectives ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Self Organizing

This study investigated whether clustering can identify different groups of students enrolled in a massive open online course (MOOC). This study applied self-organizing map and hierarchical clustering algorithms to the log files of a physics MOOC capturing how students solved weekly homework and quiz problems to identify clusters of students showing similar problem-solving patterns. The usefulness of the identified clusters was verified by examining various characteristics of students such as number of problems students attempted to solve, weekly and daily problem completion percentages, and whether they earned a course certificate. The findings of this study suggest that the clustering technique utilizing self-organizing map and hierarchical clustering algorithms in tandem can be a useful exploratory data analysis tool that can help MOOC instructors identify similar students based on a large number of variables and examine their characteristics from multiple perspectives.

Extending Self-Organizing Particle Systems to Problem Solving

2004 ◽  
Vol 10 (4) ◽  
pp. 379-395 ◽  
Author(s):  
Alejandro Rodríguez ◽  
James A. Reggia
Keyword(s):  
Problem Solving ◽  
Computer Games ◽  
Computer Animation ◽  
Movement Control ◽  
Particle Systems ◽  
Biological Phenomena ◽  
Collective Movements ◽  
Agent Teams ◽  
The Individual ◽  
Self Organizing

Self-organizing particle systems consist of numerous autonomous, purely reflexive agents (“particles”) whose collective movements through space are determined primarily by local influences they exert upon one another. Inspired by biological phenomena (bird flocking, fish schooling, etc.), particle systems have been used not only for biological modeling, but also increasingly for applications requiring the simulation of collective movements such as computer-generated animation. In this research, we take some first steps in extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles (agents) a rudimentary intelligence in the form of a very limited memory and a top-down, goal-directed control mechanism that, triggered by appropriate conditions, switches them between different behavioral states and thus different movement dynamics. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. Further, computational experiments show that collectively moving agent teams are more effective than similar but independently moving ones in carrying out such tasks, and that agent teams of either type that split off members of the collective to protect previously acquired resources are most effective. This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors. These results may prove useful not only for future modeling of animal behavior, but also in computer animation, coordinated movement control in robotic teams, particle swarm optimization, and computer games.

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