I have two classes w1 and w2 and we are working with 2D space. The density of each of the two classes are the following:

\begin{equation} p(x = {(x_{1}, x_{2})}'| w_{1}) = \frac{1}{4}\cdot e ^{-\frac{x_{1}+ x_{2}}{2}} \end{equation} \begin{equation} p(x = {(x_{1}, x_{2})}'| w_{2}) = \frac{1}{16}\cdot x_{1}x_{2} \cdot e ^{-\frac{x_{1}+ x_{2}}{2}} \end{equation}

Classes have equal prior probabilities. Solving $p(x|w_1)=p(x|w_2)$ to find the decision boundary yields the curve $x_1x_2=4$.

How can we calculate the error of the bayesian classifier? Here is the equation for the error's expected value. enter image description here


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.