Imitation learning of car driving skills with decision trees and random forests
Paweł Cichosz, Łukasz Pawełczak (2014)
International Journal of Applied Mathematics and Computer Science
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Paweł Cichosz, Łukasz Pawełczak (2014)
International Journal of Applied Mathematics and Computer Science
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Kristína Machová, Miroslav Puszta, František Barčák, Peter Bednár (2006)
Computer Science and Information Systems
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Hossein Rouhani, Arash Sadeghzadeh, Caro Lucas, Babak Araabi (2007)
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Carro, Rosa María, Pulido, Estrella, Rodríguez, Pilar (2000)
Electronic Journal of SADIO [electronic only]
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Kenji Doya, Hidenori Kimura, Aiko Miyamura (2001)
International Journal of Applied Mathematics and Computer Science
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In this paper, we introduce several system theoretic problems brought forward by recent studies on neural models of motor control. We focus our attention on three topics: (i) the cerebellum and adaptive control, (ii) reinforcement learning and the basal ganglia, and (iii) modular control with multiple models. We discuss these subjects from both neuroscience and systems theory viewpoints with the aim of promoting interplay between the two research communities.
Khaled Hussain, Varol Kaptan (2004)
RAIRO - Operations Research - Recherche Opérationnelle
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In applications such as airport operations, military simulations, and medical simulations, conducting simulations in accurate and realistic settings that are represented by real video imaging sequences becomes essential. This paper surveys recent work that enables visually realistic model constructions and the simulation of synthetic objects which are inserted in video sequences, and illustrates how synthetic objects can conduct intelligent behavior within a visual augmented reality. ...
Rafał Biedrzycki, Jarosław Arabas (2012)
International Journal of Applied Mathematics and Computer Science
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This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites....
Bartłomiej Śnieżyński (2015)
International Journal of Applied Mathematics and Computer Science
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In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for...
Peiquan Jin, Hong Chen, Xujian Zhao, Xiaowen Li, Lihua Yue (2011)
Computer Science and Information Systems
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Nefeslioglu, H.A., Sezer, E., Gokceoglu, C., Bozkir, A.S., Duman, T.Y. (2010)
Mathematical Problems in Engineering
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Zbigniew Banaszak, Oleg Zaikin (2010)
Control and Cybernetics
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Sergeja Vogrinčič, Zoran Bosnić (2011)
Computer Science and Information Systems
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