Using Derived kernel as a new Method for Recognition a Similarity Learning.
Ramadhan A. M. Alsaidi1, Ayed R.A. Alanzi2, Saleh R. A. Alenazi3, Madallah Alruwaili4
1Ramadhan A. M. Alsaidi, department of Mathematics, Jouf University, Gurayat, Saudi Arabia.
2Ayed R. A. Alanzi*, department of Mathematics, Majmaah University, Majmaah 11952, Saudi Arabia.
3Saleh R. A. Alenazi, Computer Technology department, Tabuk College of Technical, Tabuk, Saudi Arabia.
4Madallah Alruwaili, College of Computer snd Information Sciences, Jouf University, Skaka, Aljouf, Saudi Arabia.
Manuscript received on February 03, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 1974-1980 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5705029320/2020©BEIESP | DOI: 10.35940/ijeat.C5705.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Feature Extraction; Hierarchical Learning; Entropy Measures; Pearson Correlation Coefficient; Pooling Operation; Sample.