Title :
Control your information for better predictions
Author :
Moshkovitz, Michal ; Tishby, Naftali
Author_Institution :
Edmond & Lilly Safra Center for Brain Sci., Hebrew Univ. of Jerusalem, Jerusalem, Israel
fDate :
June 29 2014-July 4 2014
Abstract :
We suggest a unified view of two known prediction algorithms: Context Tree Weighting (CTW) and Prediction Suffix Tree (PST), by formulating them as information limited control problems. Using a unified view of planning and information gathering we suggest a new algorithm that combines the advantages of these two extreme algorithms and interpolates efficiently between them. The unified view is based on recent ideas from optimal control under information constraints.
Keywords :
information theory; trees (mathematics); CTW; PST; context tree weighting; prediction algorithm; prediction suffix tree; Complexity theory; Context; Context modeling; Information theory; Mathematical model; Prediction algorithms; Predictive models;
Conference_Titel :
Information Theory (ISIT), 2014 IEEE International Symposium on
Conference_Location :
Honolulu, HI
DOI :
10.1109/ISIT.2014.6874966
