Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

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.

• Publication year

  2013

• Venue

  International Journal of Advanced Research in Artificial Intelligence

• Publication date

  2013-02-01

• Fields of study

  Computer Science, Education

• Identifiers
  DOI 10.14569/IJARAI.2013.020206
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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