Big data spectra analysis using analytical programming and random decision forests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092546" target="_blank" >RIV/61989100:27240/14:86092546 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092546

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-662-45237-0_26" target="_blank" >http://dx.doi.org/10.1007/978-3-662-45237-0_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-45237-0_26" target="_blank" >10.1007/978-3-662-45237-0_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Big data spectra analysis using analytical programming and random decision forests

Popis výsledku v původním jazyce

Spectra analysis on large datasets is in focus of this paper. First of all we discuss a method useful for spectra analysis - analytical programming and its implementation. Our goal is to create mathematical formulas of emission lines from spectra, whichare characteristic for Be stars. One issue in performing this task is symbolic regression, which represents the process in our application, when measured data fits the best represented mathematical formula. In past this was only a human domain; nowadays,there are computer methods, which allow us to do it more or less effectively. A novel method in symbolic regression, compared to genetic programming and grammar evolution, is analytic programming. The aim of this work is to verify the efficiency of theparallel approach of this algorithm, using CUDA architecture. Next we will discuss parallel implementation of random decision forest (RDF) to classify huge amounts of various spectra. The mathematical formulas obtained via AP will be used

Název v anglickém jazyce

Big data spectra analysis using analytical programming and random decision forests

Popis výsledku anglicky

Spectra analysis on large datasets is in focus of this paper. First of all we discuss a method useful for spectra analysis - analytical programming and its implementation. Our goal is to create mathematical formulas of emission lines from spectra, whichare characteristic for Be stars. One issue in performing this task is symbolic regression, which represents the process in our application, when measured data fits the best represented mathematical formula. In past this was only a human domain; nowadays,there are computer methods, which allow us to do it more or less effectively. A novel method in symbolic regression, compared to genetic programming and grammar evolution, is analytic programming. The aim of this work is to verify the efficiency of theparallel approach of this algorithm, using CUDA architecture. Next we will discuss parallel implementation of random decision forest (RDF) to classify huge amounts of various spectra. The mathematical formulas obtained via AP will be used

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 8838

ISBN

978-3-662-45236-3

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

266-277

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Ho Chi Minh City

Datum konání akce

5. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—