This experiments demonstrates the Expectation Maximization (EM) algorithm. In this setting the EM is applied as a tool for classisfication.
RandomPnts
button.
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 Stop
or 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 Segment
the initial points based on your Linear/Gaussian fit by pressing