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Deep Learning Algorithm and Their Applications in the Perception Problem

EasyChair Preprint no. 2492

6 pagesDate: January 30, 2020

Abstract

The objective of this paper is to summarize a comparative account of unsupervised and supervised deep learning models and their applications. The design of a model system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples and performance evaluation. Classification plays a vital role in deep learning algorithms and 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 perception problem.

Keyphrases: Classification, deep learning, DL, perception, supervised learning, unsupervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:2492,
  author = {Redouane Lhiadi},
  title = {Deep Learning Algorithm and Their Applications in the Perception Problem},
  howpublished = {EasyChair Preprint no. 2492},

  year = {EasyChair, 2020}}
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