On Adaptive Learning HMM Classifiers Using Splitting-Merging Techniques

분할-합병기법을 이용한 HMM 분류기의 적응학습

In this paper we propose an adaptive learning method for HMM classifiers by using splitting and merging techniques to overcome the problem of the conventional teaming, where one HMM classifier per class has been trained, individually. The experimental results demonstrate a possibility that the proposed mechanism could be applied for applications of having multiple clusters in a class.