Filled in the help files with short descriptions for each algorithm. Fixed an #includ...
[mldemos:baraks-mldemos.git] / help / kpca.html
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5 <h2>Kernel Principal Component Analysis<br>
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11 Kernelized version of Principal Component Analysis<br>
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16 More information on <a href="http://en.wikipedia.org/wiki/Kernel_principal_component_analysis">Wikipedia</a>.<br>
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22 Kernel Parameters:<br>
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24 <ul>
25 <li>Kernel Type:</li><ul><li>Linear: linear kernel<br>
26     </li><li>Polynomial: polynomial kernel</li><li>RBF: radial basis function (gaussian) kernel</li></ul><li>Kernel Width: inverse variance for the kernel function, determines the radius of influence of each sample (RBF + Poly)</li><li>Degree: degree of the polynomial (Poly)</li>
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