|
|
Vstupní data a jejich význam pro vrstevnaté neuronové sítě
Gabašová, Evelina ; Mrázová, Iveta (advisor) ; Iša, Jiří (referee)
In the present work we study In some areas, artificial feed forward neural networks are still a competitive machine learning model. Unfortunately they tend to overfit the training data, which limits their ability to generalize. We study methods for regularization based on enforcing internal structure of the network. We analyze internal representations using a theoretical model based on information theory. Based on this study, we propose a regularizer that minimizes the overall entropy of internal representations. The entropy-based regularizer is computationally demanding and we use it primarily as a theoretical motivation. To develop an efficient and flexible implementation, we design a Gaussian mixture model of activations. In the experimental part, we compare our model with the existing work based on enforcement of internal representations. The presented Gaussian mixture model regularizer yields better results especially for classification tasks.
|
|
| |
|
|
Artificial neural networks for clustering and rule extraction
Iša, Jiří ; Mrázová, Iveta (advisor) ; Jiroutek, Pavel (referee)
Rule extraction with neural networks has been a common research topic over the last decades. This master thesis proposes a novel growing fuzzy inference neural network, based on the principle of growing neural structures [5]. This allows the network to adjust iteratively its number of hidden neurons. For the purpose of this network an existing clustering algorithm is enhanced to improve the sensitivity to the requested output. A novel fast weights adaptation, inspired by the fuzzy set theory, is also suggested. The characteristics of the proposed model and a new method of the selection of significant input features support the induction of a relatively small amount of simple fuzzy rules. The introduced techniques have been experimentally tested on real-world data describing the relationship between various types of housing in the Boston area and its price. The data was obtained from the "Boston housing" dataset.
|
|
|
Decision trees
Jahoda, Miroslav ; Mrázová, Iveta (advisor) ; Gregor, Petr (referee)
Among the known methods of data mining are neural networks, ILP, associative rules, Bayes networks, clustering, decision trees and others. This thesis is about decision trees, their implementation, visualization, extraction rules and the comparison of different decision trees and models of classification data in general. An integral part of the data mining process is preprocessing of data, which plays an important role and is also discussed in this thesis. Part of this thesis also concerns the comparison of different decision tree models such as CART, CHAID, C5.0 (See5) and others on a set of 3 data kinds. Finally, this model's results are compared to its results when data preprocessed by PCA analysis is used.
|
|
|
Knowledge Extraction with BP-networks
Reitermanová, Zuzana ; Mrázová, Iveta (advisor) ; Holan, Tomáš (referee)
Multi-layered neural networks of the back-propagation type are well known for their universal approximation capability. Already the standard back-propagation training algorithm used for their adjustment provides often applicable results. However, e cient solutions to complex tasks currently dealt with require a quick convergence and a transparent network structure. This supports both an improved generalization capability of the formed networks and an easier interpretation of their function later on. Various techniques used to optimize the structure of the networks like learning with hints; pruning and sensitivity analysis are expected to impact a better generalization, too. One of the fast learning algorithms is the conjugate gradient method. In this thesis, we discuss, test and analyze the above-mentioned methods. Then, we derive a new technique combining together the advantages of them. The proposed algorithm is based on the rapid scaled conjugate gradient technique. This classical method is enhanced with the enforcement of a transparent internal knowledge representation and with pruning of redundant hidden and input neurons inspired by sensitivity analysis. The performance of the developed technique has been tested on arti cial data and on real-world data obtained from the World Bank. Experiments done...
|
|
| |
|
|
Processing of secondary structures in nucleic acids
Goldwein, Robert ; Mrázová, Iveta (advisor) ; Kukačka, Marek (referee)
This work explores and studies basic methods of bioinformatics - new and perspective branch of computer science. Introduces the term Bioinformatics, familiarizes with necessary biological background (DNA and RNA molecules, proteins, central dogma of molecular biology) and also with basic bioinformatics terms (biological sequence, primary and secondary structure). It also demonstrates the implementation of basic bioinformatics algorithms and their use with real data (on Foot-and-mouth disease virus) - motif finding, longest common subsequence and sequence alignment. This work also introduces higher structures of biological sequences, primarily with secondary structure of RNA molecule.
|
|
|
Self-organization and morphing
Lessner, Daniel ; Mrázová, Iveta (advisor) ; Mráz, František (referee)
Morphing is a well-known visual effect. It is based on a uent transition of one image into another one, metamorphosis of a movie character into a bear is a possible example. Realization of such an effect requires accurate, concentrated and expensive effort of an animator. Development of tools and methods for problem solving comparable with human intelect is the subject of arti tial intelligence. Systems working with no human adjustments are often based on self-organisation. Self-organisation of a system is the appearance of complex behavior that isolated parts of the system couldn't reach. This thesis examines possibilities of application of self-organisational methods of artifi tial intelligence in morphing with the goal of reduction of the human assistence. The thesis includes information about some drafted techniques and results of experiments with the most successful technique. Experiments imply that it is possible to reach good results without human assistance if certain conditions are met.
|
|
|
The Implementation of an Artificial Intelligence in a Strategy Game Simulator
Fürbach, Radek ; Mrázová, Iveta (advisor) ; Chrpa, Lukáš (referee)
The aim of this thesis is a comparison of a few selected methods of an artificial intelligence in a specified strategy game. The thesis contains three parts. The first part specifies a model of the strategy game, whereat are simulated some experiments. It defines objects that occur in the game, relation among them, and used algorithms. The second part specifies of the artificial intelligence that is used in the strategy game. It explains the genetic algorithm and shows a few methods of so called selection, crossing, and mutation. It describes some basic artificial neural networks and their architectures. The last part describes several algorithms of the artificial intelligence using theory from the second part. It compares their efficiency on the simulated experiments.
|
|
|
Artificial Neural Networks and Their Usage For Knowledge Extraction
Petříčková, Zuzana ; Mrázová, Iveta (advisor) ; Procházka, Aleš (referee) ; Andrejková, Gabriela (referee)
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petříčková Department: Department of Theoretical Computer Science and Mathema- tical Logic Supervisor: doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Computer Science and Mathematical Logic Abstract: The model of multi/layered feed/forward neural networks is well known for its ability to generalize well and to find complex non/linear dependencies in the data. On the other hand, it tends to create complex internal structures, especially for large data sets. Efficient solutions to demanding tasks currently dealt with require fast training, adequate generalization and a transparent and simple network structure. In this thesis, we propose a general framework for training of BP/networks. It is based on the fast and robust scaled conjugate gradient technique. This classical training algorithm is enhanced with analytical or approximative sensitivity inhibition during training and enforcement of a transparent in- ternal knowledge representation. Redundant hidden and input neurons are pruned based on internal representation and sensitivity analysis. The performance of the developed framework has been tested on various types of data with promising results. The framework provides a fast training algorithm,...
|
guest ::
RSS feed.