A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset. The presented algorithm shows fast learning by utilizing time-shifted information during the progress of iterations. The computational complexity of this algorithm, which directly affects the CPU time, remains almost the same as the logarithmic one since only stored results are utilized for the speedup. Software implementation and speed are examined in the test data. The results showed that the presented method is creditable.
|ホスト出版物のタイトル||Proceedings of the International Joint Conference on Neural Networks|
|出版ステータス||Published - 2010|
|イベント||2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona|
継続期間: 2010 7 18 → 2010 7 23
|Other||2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010|
|Period||10/7/18 → 10/7/23|
ASJC Scopus subject areas