Classification Problem Solving: A Tutorial from an AI Perspective

1987 ◽  
pp. 393-409
Author(s):  
Bruce Chandrasekaran ◽  
Anne Keuneke
Keyword(s):  
Problem Solving ◽  
Classification Problem

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.

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.

Identification of parameters of an order in psychophysiology

10.12737/5514 ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 4-12
Author(s):  
Сидоркина ◽  
D. Sidorkina ◽  
Филатова ◽  
D. Filatova ◽  
Филатов ◽  
...  
Keyword(s):  
Neural Networks ◽  
Problem Solving ◽  
Brain Activity ◽  
Binary Classification ◽  
Ecological Factors ◽  
Classification Problem ◽  
Total System ◽  
System State ◽  
Psychophysiological State ◽  
Binary Classification Problem

While using a set of diagnostic characters x;, which form a total system state vector x=x(t), there emerges problem of selections such characters with more significant number l«m from the total number m. Today such problems are still not solved, therefore, the paper presents method of its solving for assessment of distinctions in psychophysiological state of two big groups of subjects (under changing ecological factors) using neuroemulators. While binary classification problem solving based on analysis of sensomotor parameters of two groups the procedure, providing real ranking x; and choosing more significant 1, is produced. The work of neuroemulator is similar to work of neural networks of hippocampus while heuristic brain activity.

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