EXTREMISM: CLASSIFICATION PROBLEMS

2021 ◽  
Author(s):  
Мархат Увайсовна Яхьяева
Keyword(s):  
Classification Problems

Современный экстремизм разнообразен по формам своего выражения. Кроме того, он может быть классифицирован по различным теоретическим основаниям (сферам жизнедеятельности, объектам направленности экстремистской активности, возрастным характеристикам субъектов экстремистской активности и т.д.). Contemporary extremism is diverse in its forms of expression. In addition, it can be classified according to various theoretical grounds (spheres of life, objects of orientation of extremist activity, age characteristics of the subjects of extremist activity, etc.).

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Equalization of the Training Set For Backpropagation Networks Applied to Classification Problems

2016 ◽  
Author(s):  
Frederico dos Santos Liporace ◽  
Ricardo José Machado ◽  
Valmir C. Barbosa
Keyword(s):  
Classification Problems ◽  
Training Set

Numerical Taxonomy in Marketing Analysis: A Review Article

1968 ◽  
Vol 5 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Ronald E. Frank ◽  
Paul E. Green
Keyword(s):  
Numerical Taxonomy ◽  
Review Article ◽  
Numerical Classification ◽  
Classification Problems ◽  
Recent Developments ◽  
Marketing Analysis

Recent developments in numerical taxonomy appear to show applicability for many classification problems in marketing. This review article describes some of the approaches to numerical classification and presents illustrative marketing applications. Current limitations of the procedures are also discussed.

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