On homotopy classification problems of J.H.C. whitehead

1985 ◽  
pp. 17-55 ◽  
Author(s):  
Hans Joachim Baues
Keyword(s):  
Classification Problems ◽  
Homotopy Classification

scholarly journals Homotopy classification of connected sums of sphere bundles over spheres, I

1981 ◽  
Vol 83 ◽  
pp. 15-36
Author(s):  
Hiroyasu Ishimoto
Keyword(s):  
Subsequent Paper ◽  
Homotopy Equivalence ◽  
Classification Problems ◽  
Homotopy Classification ◽  
Connected Sums

In the classification problems of manifolds, the connected sums of sphere bundles over spheres appear frequently. In fact, the manifolds with certain tangential and homotopy properties come to such connected sums (cf. Tamura [15], [16], Ishimoto [6], [8], [9]). Motivated by those, in this paper and the subsequent paper, we classify connected sums of sphere bundles over spheres up to homotopy equivalence by extending the results of I. M. James and J. H. C. Whitehead [10], [11], which correspond to the case that the connected sums of the above happen to be single sums. We also use Wall [17] in the case when the fibres and the base spaces of bundles are same dimensional.

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
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