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Learning with Regularization Networks
Kudová, Petra ; Neruda, Roman (advisor) ; Andrejková, Gabriela (referee) ; Hlaváčková-Schindler, Kateřina (referee)
In this work we study and develop learning algorithms for networks based on regularization theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a regularization parameter and a kernel function by minimization of cross-validation error. Two composite types of kernel functions are proposed - a sum kernel and a product kernel - in order to deal with heterogenous or large data. Three learning approaches for the RBF networks - the gradient learning, three-step learning, and genetic learning - are discussed. Based on the se, two hybrid approaches are proposed - the four-step learning and the hybrid genetic learning. All learning algorithms for the regularization networks and the RBF networks are studied experimentally and thoroughly compared. We claim that the regularization networks and the RBF networks are comparable in terms of generalization error, but they differ with respect to their model complexity. The regularization network approach usually leads to solutions with higher number of base units, thus, the RBF networks can be used as a 'cheaper' alternative in terms of model size and learning time.
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Evoluce chování inteligentních agentů v počítačových hrách
Kadlec, Rudolf ; Kudová, Petra (advisor) ; Holan, Tomáš (referee)
In the present work we study evolution of both high-level and low-level behaviour of agents in the environment of the commercial game Unreal Tournament 2004. For optimization of high-level behaviour in Deathmatch and Capture the flag game modes a new functional architecture for description of player's behaviour was designed and implemented. Then a genetic programming technique was used to optimise it. Experiments with both standard evolution schema and with coevolution are presented. In second series of experiments the NEAT algo- rithm was used to evolve low-level missile avoidance behaviour (so called "dodging").
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Evoluční algoritmy pro strukturální učení neuronových sítí
Kasík, Pavel ; Neruda, Roman (advisor) ; Kudová, Petra (referee)
Designing neural networks topologies is s complicated problem when we consider general network structures. Evolutionary algorithm can provide us with interesting solutions of this problem. This work introduces an evolutionary algorithm for evolving neural networks. One of the possible algorithms for evolving neural networks is the NEAT algorithm. The goal of this work is to modify and enhance abilities of the NEAT algorithm. Improvements are focused on utilizing position of a neuron in network, improving crossover procedure and introducing solution of algorithm parallelization that preserve abilities of both NEAT and the new algorithm.
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Mobile robot control
Franěk, Dominik ; Slušný, Stanislav (advisor) ; Kudová, Petra (referee)
The goal of this work is design and realization of an autonomous mobile robot, capable of navigation and map creation, using stereoscopic camera and robotic operation system ROS. ** This is an added text for reaching minimal length needed for uploading into information system. **
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Evoluční algoritmy pro strukturální učení neuronových sítí
Kasík, Pavel ; Neruda, Roman (advisor) ; Kudová, Petra (referee)
Designing neural networks topologies is s complicated problem when we consider general network structures. Evolutionary algorithm can provide us with interesting solutions of this problem. This work introduces an evolutionary algorithm for evolving neural networks. One of the possible algorithms for evolving neural networks is the NEAT algorithm. The goal of this work is to modify and enhance abilities of the NEAT algorithm. Improvements are focused on utilizing position of a neuron in network, improving crossover procedure and introducing solution of algorithm parallelization that preserve abilities of both NEAT and the new algorithm.
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Mobile robot control
Franěk, Dominik ; Slušný, Stanislav (advisor) ; Kudová, Petra (referee)
The goal of this work is design and realization of an autonomous mobile robot, capable of navigation and map creation, using stereoscopic camera and robotic operation system ROS. ** This is an added text for reaching minimal length needed for uploading into information system. **
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Evoluce chování inteligentních agentů v počítačových hrách
Kadlec, Rudolf ; Holan, Tomáš (referee) ; Kudová, Petra (advisor)
In the present work we study evolution of both high-level and low-level behaviour of agents in the environment of the commercial game Unreal Tournament 2004. For optimization of high-level behaviour in Deathmatch and Capture the flag game modes a new functional architecture for description of player's behaviour was designed and implemented. Then a genetic programming technique was used to optimise it. Experiments with both standard evolution schema and with coevolution are presented. In second series of experiments the NEAT algo- rithm was used to evolve low-level missile avoidance behaviour (so called "dodging").
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Learning with Regularization Networks
Kudová, Petra ; Neruda, Roman (advisor) ; Andrejková, Gabriela (referee) ; Hlaváčková-Schindler, Kateřina (referee)
In this work we study and develop learning algorithms for networks based on regularization theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a regularization parameter and a kernel function by minimization of cross-validation error. Two composite types of kernel functions are proposed - a sum kernel and a product kernel - in order to deal with heterogenous or large data. Three learning approaches for the RBF networks - the gradient learning, three-step learning, and genetic learning - are discussed. Based on the se, two hybrid approaches are proposed - the four-step learning and the hybrid genetic learning. All learning algorithms for the regularization networks and the RBF networks are studied experimentally and thoroughly compared. We claim that the regularization networks and the RBF networks are comparable in terms of generalization error, but they differ with respect to their model complexity. The regularization network approach usually leads to solutions with higher number of base units, thus, the RBF networks can be used as a 'cheaper' alternative in terms of model size and learning time.
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