Browsing by Author "Kaasla, Kaarel"

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Evaluating Slow Feature Analysis on Time-Series Data
(Tartu Ülikool, 2021) Kaasla, Kaarel; Kull, Meelis, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
In this thesis, we investigate Slow Feature Analysis (SFA) as a method of extracting slowly-varying signals from quickly-varying input data. The main aim of the thesis is two-fold. The first primary objective is to evaluate how the level of noise in input data affects the performance of SFA for different input feature combinations. The second objective of this thesis is to compare the performance of the classical formulation of SFA to a biologically plausible version of the algorithm. The first half of the thesis gives reader a theoretical overview of how the algorithm works and explores some of the previous applications. The second half conducts three experiments that explore the primary research questions of the thesis and discusses possible further research directions.
Forecasting the party support in Estonia: comparison of machine learning regression algorithms
(Tartu Ülikool, 2018) Kaasla, Kaarel; Solvak, Mihkel, juhendaja; Märtens, Kaspar, juhendaja; Tartu Ülikool. Sotsiaalteaduste valdkond; Tartu Ülikool. Johan Skytte poliitikauuringute instituut
Forecasting political behavior using economic indicators is not a very new phenomenon with the earliest literature going back as far as the 1930s. In the present day, there exists a lot of research on the topic, but the majority of these studies have been conducted in the context of a very limited number of countries such as the United States or the Western European ones. By comparison, the research on forecasting the political behavior using economic voting in Estonia is almost non-existent. This thesis will be the first in-depth study conducted at that level and forecasts the party support of the Estonian Reform Party and the Estonian Center Party using economic indicators as the predictor variables. Based on the previous economic voting theory, it has been argued that the theoretically correct model to forecast using these variables is the linear regression due to the expected associations between the economic variables and party support. However, this thesis contests this claim and argues that when analyzing the phenomena of forecasting party support using economic indicators, certain modern machine learning algorithms could be considered as legitimate alternatives to the linear regression, as each of them addresses the different shortcomings of the model. For this reason, this thesis compares the methods of linear regression, regularized linear models, autoregressive integrated moving average, and the decision-tree models to see whether the more modern approaches are able to improve upon the default linear regression model.
Seos suure viisiku teooria isiksuseomaduste ning ideoloogilise enesemääratlemise vahel Eesti näitel
(Tartu Ülikool, 2016) Kaasla, Kaarel; Solvak, Mihkel, juhendaja; Tartu Ülikool. Sotsiaalteaduste valdkond; Tartu Ülikool. Johan Skytte poliitikauuringute instituut