کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410297 679134 2007 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures
چکیده انگلیسی

Variational Bayes learning or mean field approximation is widely used in statistical models which are made of mixtures of exponential distributions, for example, normal mixtures, binomial mixtures, and hidden Markov models. To derive variational Bayes learning algorithm, we need to determine the hyperparameters in the a priori distribution; however, the design method of hyperparameters has not yet been established. In the present paper, we propose two different design methods of hyperparameters which are applied to the different purposes. In the former method, the hyperparameter is determined for minimization of the generalization error. In the latter method, it is chosen so that candidates of hidden structure in training data are extracted. It is experimentally shown that the optimal hyperparameters for two purposes are different from each other.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 74, Issue 11, May 2011, Pages 2002–2007
نویسندگان
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