scholarly journals COMPACT METRIZABLE STRUCTURES AND CLASSIFICATION PROBLEMS

2018 ◽  
Vol 83 (1) ◽  
pp. 165-186
Author(s):  
CHRISTIAN ROSENDAL ◽  
JOSEPH ZIELINSKI
Keyword(s):  
Simple Proof ◽  
Classification Problems ◽  
Affine Homeomorphism ◽  
Metric Structures

AbstractWe introduce and study the framework of compact metric structures and their associated notions of isomorphisms such as homeomorphic and bi-Lipschitz isomorphism. This is subsequently applied to model various classification problems in analysis such as isomorphism of C*-algebras and affine homeomorphism of Choquet simplices, where among other things we provide a simple proof of the completeness of the isomorphism relation of separable, simple, nuclear C*-algebras recently established by M. Sabok.

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

METHODS AND ALGORITHMS FOR THE COMPLETE PROCESSING OF A POSTDETECTOR LOW-CONTRAST OPTICAL IMAGE

2018 ◽  
pp. 99-107
Author(s):  
V. V. Lavrov ◽  
R. S. Luchkin ◽  
O. I. Nemykin ◽  
M. E. Prokhorov ◽  
Yu. G. Ryndin ◽  
...  
Keyword(s):  
Optical Image ◽  
Structural Elements ◽  
Filtration Process ◽  
Low Contrast ◽  
Information Object ◽  
Detection Stage ◽  
Metric Structures ◽  
Spot 5 ◽  
Integrated Indicators ◽  
Complete Processing

Methods and algorithms for the complete processing of a post-detector low-contrast optical image (OI) of an unknown remote object obtained by ground-based optical means of observation under conditions of a complex background situation are considered. The purpose of processing is to separate and interpret at least with the help of the analyst, of the main constructive elements using the integrated indicators introduced in [6] and the characteristics of the analyzed OI, which are connected by the information, topological and metric structures of the OI. The stages of processing the OI include extracting the image-containing information object of the image portion (detection) and filtration of the OI, using recursive rank filtering. The final stages of processing include the segmentation of the OI and the allocation on it constructive elements using the apparatus of graph theory. An example of image processing of a Spot-5 spacecraft obtained in real conditions is given. It is shown that in this case at the detection stage it is possible to reduce the volume of information processed at subsequent stages by 8 times, in the filtration process to increase the compactness of the OI and to increase its connectivity in comparison with the post-detection OI. As a result of segmentation and allocation of constructive elements, three structural elements that can be interpreted as a spacecraft case and two remote panels can be identified with the analyst’s participation.

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