动态时间归整
隐马尔可夫模型
计算机科学
稳健性(进化)
人工智能
熵(时间箭头)
分类器(UML)
模式识别(心理学)
语音识别
生物化学
量子力学
基因
物理
化学
作者
Nesma Houmani,Sonia Garcia-Salicetti,Bernadette Dorizzi
标识
DOI:10.1109/btas.2009.5339074
摘要
In this work, we study different combinations of the five time functions captured by a digitizer in presence or not of time variability. To this end, we propose two criteria independent of the classification step: personal entropy, introduced in our previous works and an intra-class variability measure based on dynamic time warping. We confront both criteria to system performance using a hidden Markov model (HMM) and dynamic time warping (DTW). Moreover, we introduce the concept of short-term time variability, proposed on MCYT-100, and long-term time variability studied with BIOMET database. Our experiments clarify conflicting results in the literature and confirm some other: pen inclination angles are very unstable in presence or not of time variability; the only combination which is robust to time variability is that containing only coordinates; finally, pen pressure is not recommended in the long-term context, although it may give better results in terms of performance (according to the classifier used) in the short-term context.
科研通智能强力驱动
Strongly Powered by AbleSci AI