Deep Learning Algorithm and Their Applications in the Perception Problem

EasyChair Preprint 2492

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, DL, deep learning, perception, supervised learning, unsupervised learning

@booklet{EasyChair:2492,
  author    = {Redouane Lhiadi},
  title     = {Deep Learning Algorithm and Their Applications in the Perception Problem},
  howpublished = {EasyChair Preprint 2492},
  year      = {EasyChair, 2020}}