隐马尔可夫模型的调整维特比训练
The adjusted Viterbi training for hidden Markov models
摘要：
EM方法是隐马尔可夫模型参数估计的主要工具。然而，应用程序通过Viterbi提取或训练（VT）来取代EM。VT在计算上不太密集，更稳定，并且具有更多的直观吸引力，但是VT估计是有偏差的，并且不满足以下不动点性质。假设，给定无限大的样本并初始化为真实参数，VT通常会远离初始值。我们提出了调整维特比训练（VA），一种新的方法来恢复固定点的属性，从而减轻整体不精确的VT估计，同时保留基线VT算法的计算优势。其他地方的模拟表明，VA在混合模型和更一般的障碍的特殊情况下都明显提高了估计的精度。然而，完全分析，VA校正依赖于无限维特比比对和相关的限制概率分布。虽然在混合物的情况下明确，这些限制措施的存在是不明显的更一般的障碍。本文证明了在一定的条件下，一般的Hu的极限分布是存在的。
英文摘要：
The EM procedure is a principal tool for parameter estimation in the hidden Markov models. However, applications replace EM by Viterbi extraction, or training (VT). VT is computationally less intensive, more stable and has more of an intuitive appeal, but VT estimation is biased and does not satisfy the following fixed point property. Hypothetically, given an infinitely large sample and initialized to the true parameters, VT will generally move away from the initial values. We propose adjusted Viterbi training (VA), a new method to restore the fixed point property and thus alleviate the overall imprecision of the VT estimators, while preserving the computational advantages of the baseline VT algorithm. Simulations elsewhere have shown that VA appreciably improves the precision of estimation in both the special case of mixture models and more general HMMs. However, being entirely analytic, the VA correction relies on infinite Viterbi alignments and associated limiting probability distributions. While explicit in the mixture case, the existence of these limiting measures is not obvious for more general HMMs. This paper proves that under certain mild conditions, the required limiting distributions for general HMMs do exist.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
