Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td align="left"></td><td align="center"> Colon cancer</td><td align="center"> Breast cancer</td><td align="center"> Leukemia cancer</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td align="left"> P.C.</td><td align="center"> 8.9108</td><td align="center">8.7721</td><td align="center"> 5.2066</td></tr><tr><td align="left">  D.C.B.</td><td align="center">9.0529</td><td align="center">8.7889</td><td align="center"> 5.5114</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td>Here PC stands for the parsimonious correlation we proposed, DCB is the novel kernel proposed by us representing denoised correlation-based kernel.<br/></td></tr></table>

Dominant eigenvalues for parsimonious correlation Kernel and Denoised Correlation Based Kernel.

Computational and Mathematical Methods in Medicine

tab4

Table 4

Table 4: Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data 