1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchMartin Szummerhttps://www.microsoft.com/en-us/research/people/szummer/Bayesian Conditional Random Fields - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="D1NvrNS6GV"><a href="https://www.microsoft.com/en-us/research/publication/bayesian-conditional-random-fields/">Bayesian Conditional Random Fields</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/bayesian-conditional-random-fields/embed/#?secret=D1NvrNS6GV" width="600" height="338" title="“Bayesian Conditional Random Fields” — Microsoft Research" data-secret="D1NvrNS6GV" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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We propose Bayesian Conditional Random Fields (BCRFs) for classifying interdependent and structured data, such as sequences, images or webs. BCRFs are a Bayesian approach to training and inference with conditional random fields, which were previously trained by maximizing likelihood (ML) (Lafferty et al., 2001). Our framework avoids the problem of overfitting, and offers the full […]