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.
Users
Please
log in to take part in the discussion (add own reviews or comments).