Hello,
Does DSL provide any functions to set or change the maximum iterations and criterion for convergence in parameter learning using static EM? And what is the default setting for the maximum iteration in static EM?
Thank you,
Bo
How to set maximum iterations and criterion for convergence in parameter learning
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Re: How to set maximum iterations and criterion for convergence in parameter learning
Unfortunately, SMILearn currently uses a hardcoded convergence crtiterion in its EM implementation. The next release planned for late spring this year will fix this issue.Does DSL provide any functions to set or change the maximum iterations and criterion for convergence in parameter learning using static EM? And what is the default setting for the maximum iteration in static EM?
Just for the record, SMILE and SMILearn are now developed by BayesFusion, not by Decision Support Laboratory. We keep the DSL_ prefix on the library class names for backward compatibility.
Re: How to set maximum iterations and criterion for convergence in parameter learning
So, at this point, the maximum number of iteration cannot be re-set either, right?
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Re: How to set maximum iterations and criterion for convergence in parameter learning
EM in SMILearn does not have a check based on iteration count. The exit from main EM loop is based on the ratio of log likelihood from previous and current iteration. The threshold ratio, as mentioned earlier in this thread, is currently hardcoded.