Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

By R. Sathya, J. Nivas, Annamma Abraham.. 2013

418 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/54a5679a668d4942087aae79b31a7a8584553416

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, background): “This learning algorithm can learn and organize information without providing error signals to evaluate the potential solution [68].”