The linear kernel is what you would expect, a linear model. I believe that the polynomial kernel is similar, but the boundary is of some defined but arbitrary order (e.g. y = b1 + b2X + b3X^2 + b4X^3).
RBF uses normal curves around the data points, and sums these so that the decision boundary can be defined by a type of topology condition such as curves where the sum is above a value of 0.5. (see this picture )
I am not certain what the sigmoid kernel is, unless it is similar to the logistic regression model where a logistic function is used to define curves according to where the logistic value is greater than some value (modeling probability), such as 0.5 like the normal case.