Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for subject:(graafiset mallit). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Raiko, Tapani. Bayesian Inference in Nonlinear and Relational Latent Variable Models.

Degree: 2006, Helsinki University of Technology

Statistical data analysis is becoming more and more important when growing amounts of data are collected in various fields of life. Automated learning algorithms provide a way to discover relevant concepts and representations that can be further used in analysis and decision making. Graphical models are an important subclass of statistical machine learning that have clear semantics and a sound theoretical foundation. A graphical model is a graph whose nodes represent random variables and edges define the dependency structure between them. Bayesian inference solves the probability distribution over unknown variables given the data. Graphical models are modular, that is, complex systems can be built by combining simple parts. Applying graphical models within the limits used in the 1980s is straightforward, but relaxing the strict assumptions is a challenging and an active field of research. This thesis introduces, studies, and improves extensions of graphical models that can be roughly divided into two categories. The first category involves nonlinear models inspired by neural networks. Variational Bayesian learning is used to counter overfitting and computational complexity. A framework where efficient update rules are derived automatically for a model structure given by the user, is introduced. Compared to similar existing systems, it provides new functionality such as nonlinearities and variance modelling. Variational Bayesian methods are applied to reconstructing corrupted data and to controlling a dynamic system. A new algorithm is developed for efficient and reliable inference in nonlinear state-space models. The second category involves relational models. This means that observations may have distinctive internal structure and they may be linked to each other. A novel method called logical hidden Markov model is introduced for analysing sequences of logical atoms, and applied to classifying protein secondary structures. Algorithms for inference, parameter estimation, and structural learning are given. Also, the first graphical model for analysing nonlinear dependencies in relational data, is introduced in the thesis.

Tilastollisen tietojenkäsittelyn merkitys on vahvassa kasvussa, sillä tietoaineistoa kerätään yhä enemmän lukuisilla eri aloilla. Automaattisilla oppivilla menetelmillä voidaan löytää merkityksellisiä käsitteitä ja esitysmuotoja, joita voidaan edelleen käyttää analysoinnissa ja päätöksenteossa. Tärkeä tilastollisen koneoppimisen menetelmäperhe, graafiset mallit, on selkeästi tulkittavissa ja sillä on hyvä teoreettinen perusta. Graafinen malli koostuu verkosta, jonka solmut kuvaavat satunnaismuuttujia ja linkit määrittelevät niiden väliset riippuvuussuhteet. Bayesiläinen päättely ratkaisee tuntemattomien muuttujien jakauman aineiston ehdolla. Graafiset mallit ovat modulaarisia, eli monimutkaisia järjestelmiä voidaan rakentaa yhdistelemällä yksinkertaisia osia. 1980-luvun tiukkojen oletusten puitteissa graafisten mallien soveltaminen on suoraviivaista, mutta näiden oletusten väljentäminen on…

Advisors/Committee Members: Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science.

Subjects/Keywords: machine learning; graphical models; probabilistic reasoning; nonlinear models; variational methods; state-space models; hidden Markov models; inductive logic programming; first-order logic; koneoppiminen; graafiset mallit; todennäköisyyslaskentaan perustuva päättely; epälineaariset mallit; variaatiomenetelmät; tila-avaruusmallit; piilo-Markov -malli; induktiivinen logiikkaohjelmointi; ensimmäisen kertaluvun logiikka

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Raiko, T. (2006). Bayesian Inference in Nonlinear and Relational Latent Variable Models. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2006/isbn951228510X/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Raiko, Tapani. “Bayesian Inference in Nonlinear and Relational Latent Variable Models.” 2006. Thesis, Helsinki University of Technology. Accessed August 18, 2019. http://lib.tkk.fi/Diss/2006/isbn951228510X/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Raiko, Tapani. “Bayesian Inference in Nonlinear and Relational Latent Variable Models.” 2006. Web. 18 Aug 2019.

Vancouver:

Raiko T. Bayesian Inference in Nonlinear and Relational Latent Variable Models. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2019 Aug 18]. Available from: http://lib.tkk.fi/Diss/2006/isbn951228510X/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raiko T. Bayesian Inference in Nonlinear and Relational Latent Variable Models. [Thesis]. Helsinki University of Technology; 2006. Available from: http://lib.tkk.fi/Diss/2006/isbn951228510X/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.