کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1148867 | 957854 | 2013 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimal Viterbi Bayesian predictive classification for data from finite alphabets
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
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چکیده انگلیسی
A family of Viterbi Bayesian predictive classifiers has been recently popularized for speech recognition applications with continuous acoustic signals modeled by finite mixture densities embedded in a hidden Markov framework. Here we generalize such classifiers to sequentially observed data from multiple finite alphabets and derive the optimal predictive classifier under exchangeability of the emitted symbols. We demonstrate that the optimal predictive classifier which learns from unlabelled test items improves considerably upon marginal maximum a posteriori rule in the presence of sparse training data. It is shown that the learning process saturates when the amount of test data tends to infinity, such that no further gain in classification accuracy is possible upon arrival of new test items in the long run.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 143, Issue 2, February 2013, Pages 261-275
Journal: Journal of Statistical Planning and Inference - Volume 143, Issue 2, February 2013, Pages 261-275
نویسندگان
Jukka Corander, Jie Xiong, Yaqiong Cui, Timo Koski,