Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
R. Sathya,Annamma Abraham +1 more
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TLDR
Though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.Abstract:
This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.read more
Citations
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References
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On learning algorithm selection for classification
Shawkat Ali,Kate A. Smith +1 more
TL;DR: This paper introduces a new method for learning algorithm evaluation and selection, with empirical results based on classification, to generate rules, using the rule-based learning algorithm C5.0, to describeWhich types of algorithms are suited to solving which types of classification problems.