New Measure of Boolean Factor Analysis Quality

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00359156" target="_blank" >RIV/67985807:_____/11:00359156 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-20282-7_11" target="_blank" >http://dx.doi.org/10.1007/978-3-642-20282-7_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-20282-7_11" target="_blank" >10.1007/978-3-642-20282-7_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

New Measure of Boolean Factor Analysis Quality

Popis výsledku v původním jazyce

Learning of objects from complex patterns is a long-term challenge in philosophy, neuroscience, machine learning, data mining, and in statistics. There are some approaches in literature trying to solve this difficult task consisting in discovering hiddenstructure of high-dimensional binary data and one of them is Boolean factor analysis. However there is no expert independent measure for evaluating this method in terms of the quality of solutions obtained, when analyzing unknown data. Here we propose information gain, model-based measure of the rate of success of individual methods. This measure presupposes that observed signals arise as Boolean superposition of base signals with noise. For the case whereby a method does not provide parameters necessary for information gain calculation we introduce the procedure for their estimation. Using an extended version of the Bars Problem generation of typical synthetics data for such a task, we show that our measure is sensitive to all types o

Název v anglickém jazyce

New Measure of Boolean Factor Analysis Quality

Popis výsledku anglicky

Learning of objects from complex patterns is a long-term challenge in philosophy, neuroscience, machine learning, data mining, and in statistics. There are some approaches in literature trying to solve this difficult task consisting in discovering hiddenstructure of high-dimensional binary data and one of them is Boolean factor analysis. However there is no expert independent measure for evaluating this method in terms of the quality of solutions obtained, when analyzing unknown data. Here we propose information gain, model-based measure of the rate of success of individual methods. This measure presupposes that observed signals arise as Boolean superposition of base signals with noise. For the case whereby a method does not provide parameters necessary for information gain calculation we introduce the procedure for their estimation. Using an extended version of the Bars Problem generation of typical synthetics data for such a task, we show that our measure is sensitive to all types o

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

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Adaptive and Natural Computing Algorithms. Part I

ISBN

978-3-642-20281-0

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

100-109

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Ljubljana

Datum konání akce

14. 4. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000302389300011