1) multiple observations
多观察序列
1.
This paper proposes a HMM training algorithm which is based on grouping multiple observations by multiple correlation coefficient .
在用多观察序列训练HMM理论的基础上,提出了一种基于对多观察序列按多相关系数分组的HMM训练算法(简称基于多相关分组的HMM训练算法)。
2.
This paper proposes a theoretical justification of the multiple observations HMMs training algorithm that does not impose the observation independence assumption and shows that the traditional BaumWelch algorithm is only a spec.
本文在不附加任何假设的前提下,提出了一种用多观察序列训练HMM的算法,从理论上解决了上述问题。
3) observation vector sequence
观察矢量序列
4) Multiple Observations
多观测序列
1.
The Baum-Welch Algorithm of HMM2 with Multiple Observations;
多观测序列HMM2的Baum-Welch算法
2.
Results It proposes the structure of second-order HMM(HMM2) on condition that observation noise is not independent of the Markov chain,and obtain the Baum-Welch algorithm of the model on condition that multiple observations is not independent.
结果给出了在观测噪声和马尔可夫链不相互独立条件下二阶隐马尔可夫模型(second-or-der HMM:HMM2)的结构,获得了在多观测序列不相互独立的情况下HMM2的Baum-Welech学习算法。
5) dependence of observable sequences
可观察序列的相依性
6) fuzzy observation sysbolic sequence
模糊观察符号序列
补充资料：观察(见科学观察)
观察(见科学观察)
observation
吝。guaneho观察(observation)见科学观
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条