Author: Saban Ozturk; Umut Ozkaya; Mucahid Barstugan
Title: Classification of Coronavirus Images using Shrunken Features Document date: 2020_4_6
ID: 2l1zw19o_31
Snippet: An AE architecture consists of two layers, encoder, and decoder, whose main purpose is to re-interpret the relationship between input and output. In the proposed study, AE architecture expresses the high-dimensional feature vector with fewer parameters. AE architecture, trained in an unsupervised style, creates a relationship between input and output. In AE architecture, more than one AE is added in series. Let X={xn} N be an N feature vector. Ne.....
Document: An AE architecture consists of two layers, encoder, and decoder, whose main purpose is to re-interpret the relationship between input and output. In the proposed study, AE architecture expresses the high-dimensional feature vector with fewer parameters. AE architecture, trained in an unsupervised style, creates a relationship between input and output. In AE architecture, more than one AE is added in series. Let X={xn} N be an N feature vector. New shrunken feature vectors to be obtained at the sAE output F:X→[0,1] Nxk , is a k-bit vector (bnϵ[0,1] k ) for each feature vector xn.
Search related documents:
Co phrase search for related documents- feature vector and propose study: 1
- output input and propose study: 1, 2
Co phrase search for related documents, hyperlinks ordered by date