Showing posts with label Bayesian regularization. Show all posts
Showing posts with label Bayesian regularization. Show all posts

Wednesday, October 31, 2018

Bayesian regularization for RBFN

Bayesian regularization for RBFN

 2 groups of linearly inseparable data (A,B) are defined in the 2-dimensional input space. The task is to define a neural network for solving the XOR classification problem.
K = 100; % offset of clusters q = .6; % define 2 groups of input data A = [rand(1,K)-q rand(1,K)+q; rand(1,K)+q rand(1,K)-q]; B = [rand(1,K)+q rand(1,K)-q; rand(1,K)+q rand(1,K)-q]; % plot data plot(A(1,:),A(2,:),'k+',B(1,:),B(2,:),'b*') grid on hold on
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Black-Scholes formula-R

 Black-Scholes formula-R > BlackScholes <- function(TypeFlag = c("c", "p"), S, X, Time, r, b, sigma) { TypeFla...