publication . Article . 2020

Using Derived kernel as a new Method for Recognition a Similarity Learning.

Ramadhan A. M. Alsaidi; Ayed R. A. Alanzi; Saleh Rehiel A. Alenazi; Madallah M Alruwaili;
Open Access
  • Published: 28 Feb 2020 Journal: International Journal of Engineering and Advanced Technology, volume 9, pages 1,974-1,980 (eissn: 2249-8958, Copyright policy)
  • Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Abstract
A new technique for feature withdrawal by neural response is going to be familiarized in this research work by merging an entropy measure with Squared Pearson correlation Coefficient (SPCC) method. The process of choosing effective models on the basis of entropy measures was proposed further to enhance the ability to select templates. For more accurate similarity measure we used the statistical significant relationship between functions. The research illustrate that the proposed method is proficiently compared with the state-of-the-art methods.
Persistent Identifiers
Subjects
free text keywords: Computer Science Applications, General Engineering, Environmental Engineering, Feature Extraction; Hierarchical Learning; Entropy Measures; Pearson Correlation Coefficient; Pooling Operation; Sample., 2249-8958, C5705029320/2020©BEIESP, Feature extraction, Kernel (statistics), Pattern recognition, Pearson product-moment correlation coefficient, symbols.namesake, symbols, Artificial intelligence, business.industry, business, Similarity learning, Sample (graphics), Mathematics
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