Optimization Framework with Minimum Description Length Principle for Probabilistic Programming

Autor:	Sergey Rodionov, Vita Batishcheva, Alexey Potapov
Rok vydání:	2015
Předmět:	Task (computing) Theoretical computer science Computer science Bayesian probability Probabilistic logic Sampling (statistics) Genetic programming occam Overfitting Minimum description length Algorithm computer computer.programming_language
Zdroj:	Artificial General Intelligence ISBN: 9783319213644 AGI
DOI:	10.1007/978-3-319-21365-1_34
Popis:	Application of the Minimum Description Length principle to optimization queries in probabilistic programming was investigated on the example of the C++ probabilistic programming library under development. It was shown that incorporation of this criterion is essential for optimization queries to behave similarly to more common queries performing sampling in accordance with posterior distributions and automatically implementing the Bayesian Occam's razor. Experimental validation was conducted on the task of blood cell detection on microscopic images. Detection appeared to be possible using genetic programming query, and automatic penalization of candidate solution complexity allowed to choose the number of cells correctly avoiding overfitting.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5c077f5b9f86a804a28318f5556eea04 https://doi.org/10.1007/978-3-319-21365-1_34 Zobrazit plný text záznamu