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Author:Vainonen, Antti
Title:Spinlasimallit data-analyysissä
Publication type:Master's thesis
Publication year:2010
Pages:47 s.      Language:   fin
Department/School:Informaatio- ja luonnontieteiden tiedekunta
Main subject:Tietojenkäsittelyteoria   (T-79)
Supervisor:Orponen, Pekka
Instructor:Orponen, Pekka
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
Instructions

Reading digital theses in the closed network of the Aalto University Harald Herlin Learning Centre

In the closed network of Learning Centre you can read digital and digitized theses not available in the open network.

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Location:P1 Ark Aalto     | Archive
Keywords:spinglass model
data-analysis
simulated annealing
learning of interactions
spinlasimalli
data-analyysi
simuloitu jäähdytys
vuorovaikutusten oppiminen
Abstract (eng): In this work the application of spinglass models in analysing interactions in binary data was researched.
A procedure for learning binary interactions in a given dataset was developed.

The developed procedure is based in bayesian maximum-likelihood estimation.
The best estimated model was searched with simulated annealing.
Possibilities for optimisation of the simulated annealing algorithm were also investigated.

Test data was generated with sampling methods such as Metropolis sampling, Gibbs sampling and the Propp-Wilson algorithm.

The experimental part of the work was restricted to small models, with which good learning results were achieved.
Possibilities for further research and applicability of the method are discussed.
Abstract (fin): Työssä tutkittiin spinlasimallien soveltuvuutta data-analyysiin mallintamaan binäärisiä vuorovaikutuksia.
Työssä kehitettiin menetelmä, jolla annetusta datajoukosta voidaan oppia siinä esiintyvät binääriset vuorovaikutukset.

Kehitetty menetelmä perustuu bayesiläiseen suurimman uskottavuuden maksimointiin.
Parhaan selittävän mailin haussa käytettiin simuloitua jäähdytystä, jonka toiminnan optimointimahdollisuuksia myös selvitettiin.

Testidatan generointia varten käytettiin erilaisia otantamenetelmiä, kuten Metropolis- ja Gibbs- otantaa, sekä Propp-Wilson -algoritmia.

Työn kokeellinen osuus rajoittui pienikokoisiin malleihin, joilla saavutettiin hyviä oppimistuloksia.
Tutkielman lopussa tarkastellaan jatkotutkimusmahdollisuuksia ja menetelmän käyttökelpoisuutta.
ED:2010-09-02
INSSI record number: 40387
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