Journal article Open Access

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

Ramadhan A. M. Alsaidi,; Ayed R.A. Alanzi; Saleh R. A. Alenazi; Madallah Alruwaili

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:contributor>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</dc:contributor>
  <dc:creator>Ramadhan A. M. Alsaidi,</dc:creator>
  <dc:creator>Ayed R.A. Alanzi</dc:creator>
  <dc:creator>Saleh R. A. Alenazi</dc:creator>
  <dc:creator>Madallah Alruwaili</dc:creator>
  <dc:description>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.</dc:description>
  <dc:source>International Journal of Engineering and Advanced Technology (IJEAT) 9(3) 1994-1980</dc:source>
  <dc:subject>Feature Extraction; Hierarchical Learning; Entropy Measures; Pearson Correlation Coefficient; Pooling Operation; Sample.</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>Using Derived kernel as a new Method for Recognition a Similarity Learning.</dc:title>
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