This experiments demonstrates the Expectation Maximization (EM) algorithm. In this setting the EM is applied as a tool for classisfication.
RandomPntsbutton.
InitKernels to get a different starting condition (EM algorithm is VERY sensitive
to the starting conditions!)
EM Run to start the algorithm. Observe the evolution of the process (convergence is guaranteed!)
EM Stop or EM 1 Step to terminate the or take one step at a time
You can Segmen+ "This experiments demonstrates the Expectation Maximization (EM) algorithm. In this setting the EM is applied as a tool for classisfication.
RandomPnts button.
nitKernels to get a different starting condition (EM algorithm is VERY sensitive
to the starting conditions!)
EM Run to start the algorithm. Observe the evolution of the process (convergence is guaranteed!)
EM Stopor EM 1 Step to terminate the or take one step at a time
You can Segment the initial points based on your Linear/Gaussian fit by pressing Segmentthe initial points based on your Linear/Gaussian fit by pressing