I was looking at Support Vector machines (SVM) kernels. Looking at Polynomial Kernel and Kernel Perceptron I was curious how they differ?Work DonePolynomial Kernel: $d_{k+1}(x)=d_{k}(\bar{x})+\rho k(\bar{x}_{k}, \bar{x})\; \mbox{if}\; \bar{x}_k\epsilon\,C_1$$d_{k+1}(x)=d_{k}(\bar{x})-\rho k(\bar{x}_{k}, \bar{x})\; \mbox{if}\; \bar{x}_k\epsilon\,C_2$where $k(\bar{x}_{k}, \bar{x}) = (\bar{x}\cdot \bar{x}_{k}+1)^{q}$ Kernel Perceptron:This is given by$g(x)=\sum_{j=1}^{N}a_{j} K(\bar{x},\bar{x}_j)$So as per my understanding a bias constant is added in former case when compared to later. So how does that impact and what difference does it make? Or am I missing something?Any insights are appreciated.
Kernel Perceptron vs Polynomial Perceptron
machine learning;pattern recognition;kernel functions;svm