摘要翻译：
提出并分析了离散隐马尔可夫模型(HMMs)中的三种在线学习算法，并与Baldi-肖万算法进行了比较。利用Kullback-Leibler散度作为推广误差的度量，我们画出了简化情况下的学习曲线。分析了其中一种算法在学习漂移概念时的性能，并与Baldi-Harris算法在相同情况下的学习性能进行了比较。根据我们的结果，对学习和对称破缺作了简要的讨论。
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英文标题：
《Online Learning in Discrete Hidden Markov Models》
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作者：
Roberto C. Alamino, Nestor Caticha
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  We present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented.
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PDF链接：
https://arxiv.org/pdf/708.2377