Optimization is an important part of mathematics and is mostly used for practical applications. For specific types of objective functions, a lot of different methods exist. A method to use when the objective is unknown and/or expensive can be difficult to determine. One of the answers is bayesian optimization, which instead of direct optimization creates a probabilistic model and uses it to constructs easily optimizable auxiliary function. It is an iterative method that uses information from previous iterations to find new point in which the objective is evaluated and tries to find the optimum within a fewer iterations. This thesis introduces bayesian optimization, suma- rizes its different approaches in lower and higher dimensions and shows when to use it suitably. An important part of the thesis is my own optimization algorithm which is applied to different practical problems - e.g. parameter optimization in machine learning algorithm. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:367593 |
| Date | January 2017 |
| Creators | Kostovčík, Peter |
| Contributors | Lachout, Petr, Hlubinka, Daniel |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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