Srovnání paradigmat strojového učení

Abstract

The aim of this thesis is to summarize the theory of machine learning in multi-agent systems, their basic paradigms and compare the basic features and functionalities of selected machine learning methods through implementation. The first part of the thesis deals with the introduction of machine learning and the description of each paradigm. The second part devoted to a case study.

Description

Subject(s)

machine learning, agent, multiagent system, symbol-based, genetic algorithms, stochastic, connectionist approaches

Citation