A distributed context-free grammars learning algorithm and its application in video classification
Jing Huang, Dan Schonfeld · 2012
In this paper, we propose a novel statistical estimation algorithm to stochastic context-sensitive grammars (SCSGs). First, we show that the SCSGs model can be solved by decomposing it into several causal stochastic context-free grammars (SCFGs) models and each of these SCFGs models can be solved simultaneously using a fully synchronous distributed computing framework. An alternate updating scheme based approximate solution to multiple SCFGs is also provided under the assumption of a realistic sequential computing framework. A series of statistical algorithms are expected to learn SCFGs subsequently. The SGSCs can be then used to represent multiple-trajectory. Experimental results demonstrate the improved performance of our method compared with existing methods for multiple-trajectory classification.