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  • wangxinxi 11:37 on January 30, 2014 Permalink | Reply
    Tags: , graphical model,   

    In general, you can avoid getting ill-conditioned covariance matrices by using one of the following precautions:

    Pre-process your data to remove correlated features.
    Set ‘SharedCov’ to true to use an equal covariance matrix for every component.
    Set ‘CovType’ to ‘diagonal’.
    Use ‘Regularize’ to add a very small positive number to the diagonal of every covariance matrix.
    Try another set of initial values.

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  • wangxinxi 14:54 on October 4, 2012 Permalink | Reply
    Tags: graphical model,   

    看了Stochastic Variational Inference这篇paper之后,突然有一种热血沸腾的感觉。

     
  • wangxinxi 19:48 on April 6, 2011 Permalink | Reply
    Tags: CRF, graphical model, HMM,   

    Copied from An Introduction to Conditional Random Fields for Relational Learning

    http://www.cs.cmu.edu/~zhuxj/courseproject/graphical.html
    http://pages.cs.wisc.edu/~jerryzhu/

     
  • wangxinxi 19:23 on March 13, 2011 Permalink | Reply
    Tags: , graphical model,   

    Stanford的Andrew Ng讲的EM真是清晰啊。终于帮我搞定了一个graphical model训练的推导。

     
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