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Automatické osvojení vzorů s minimální supervizí
Klíč, Radoslav ; Hana, Jiří (advisor) ; Hlaváčová, Jaroslava (referee)
The thesis presents a semi-supervised morphology learner developed by extending Paramor (Monson, 2009), an unsupervised system, to accept easy to obtain manually provided data in the form of inflections with marked morpheme boundary. In addition, a hierarchical clustering framework allowing combination of multiple sources of information was developed as a part of the thesis. The approach was tested on Czech, Slovene, German and Catalan and has shown increased F-measure in comparison with the Paramor baseline.
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Detekce podezřelých anotací
Václ, Jan ; Vidová Hladká, Barbora (advisor) ; Hana, Jiří (referee)
This work describes a machine learning approach for checking the part-of-speech annotation, and presents its implementation - a system called MissTagger. The checking procedure covers both error detection and error correction. MissTagger employs a simplified instance-based learning algorithm where the words in the text are recognized as instances. Part-of-speech tags of context of static length are selected as features, no lexical information is included. The words whose tags comprises this context are chosen based either on a linear or on a dependency-tree structure of the sentence. Two languages are examined in the experiments for evaluation, Czech and English.
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Automatic Resolution of Pronoun Coreference in Czech
Košarko, Ondřej ; Mírovský, Jiří (advisor) ; Vidová Hladká, Barbora (referee)
Title: Automatic Resolution of Pronoun Coreference in Czech Author: Ondřej Košarko Department: ÚFAL MFF UK Supervisor: RNDr. Jiří Mírovský, Ph.D. Supervisor's email address: mirovsky@ufal.mff.cuni.cz Abstract: The aim of this thesis is to introduce a procedure for automatic pronomial coreference resolution in Czech texts. The text is morphologically and analytically annotated acording to the system of Prague Dependency Treebank. The procedure uses a machine learning method; for its training a set of manually annotated data from Prague Dependency Treebank is used. Evaluation of the results is also part of this thesis. Keywords: pronomial coreference, automatic resolution, machine learning
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Machine learning-based identification of separating features in molecular fragments
Ravi, Aakash ; Hoksza, David (advisor) ; Škoda, Petr (referee)
Chosen molecular representation is one of the key parameters of virtual screening campaigns where one is searching in-silico for active molecules with respect to given macromolecular target. Most campaigns employ a molecular representation in which a molecule is represented by the presence or absence of a predefined set of topological fragments. Often, this information is enriched by physiochemical features of these fragments: i.e. the representation distinguishes fragments with identical topology, but different features. Given molecular representation, however, most approaches always use the same static set of features irrespective of the specific target. The goal of this thesis is, given a set of known active and inactive molecules with respect to a target, to study the possibilities of parameterization of a fragment-based molecular representation with feature weights dependent on the given target. In this setting, we are given a very general molecular representation, with targets represented by sets of known active and inactive molecules. We subsequently propose a machine-learning approach that would identify which of the features are relevant for the given target. This will be done using a multi-stage pipeline that includes data preprocessing using statistical imputation and dimensionality...
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License Plate Detection and Recognition
Řepka, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This paper addresses the problem of object detection and recognition from still images using methods of computer vision. The objects of detection are czech license plates and the goal of this paper was to create an automatic license plate anotation tool. Suggested solution uses edge detection and machine learned cascading classifiers. Created application was then tested on dataset taken by the author.
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Accelerated Neural Networks on GPU
Tomko, Martin ; Zachariášová, Marcela (referee) ; Krčma, Martin (advisor)
This thesis deals with the implementation of an application for artificial neural networks simulation and acceleration using a graphics processing unit. The computation and training of feedforward neural networks using the Backpropagation algorithm are the main focus of this thesis, but the application also supports other network types, and it makes it possible to extend the application with different training algorithms. Next, the application allows us to create neural networks with structural anomalies, and thus, to test the neural network's fault tolerance. The application is implemented in the C++ language, using OpenCL to manage GPU computation. The Backpropagation acceleration results were compared with the free open source library FANN.
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Gradient Boosting Machine and Artificial Neural Networks in R and H2O
Sabo, Juraj ; Bašta, Milan (advisor) ; Plašil, Miroslav (referee)
Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.
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AI techniques in algorhitmic trading
Šmejkal, Oldřich ; Pavlíčková, Jarmila (advisor) ; Berka, Petr (referee)
Diploma thesis is focused on research and description of current state of machine learning field, focusing on methods that can be used for prediction and classification of time series, which could be then applied in the algorithmic trading field. Reading of theoretical section should explain basic principles of financial markets, algorithmic trading and machine learning also to reader, which was previously familiar with the subject only very thoroughly. Main objective of application part is to choose appropriate methods and procedures, which match current state of art techniques in machine learning field. Next step is to apply it to historical price data. Result of application of selected methods is determination of their success at out of sample data that was not used during model calibration. Success of prediction was evaluated by accuracy metric along with Sharpe ratio of basic trading strategy that is based on model predictions. Secondary outcome of this work is to explore possibilities and test usability of technologies used in application part. Specifically is tested and used SciPy environment, that combines Python with packages and tools designed for data analysis, statistics and machine learning.
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On possible approaches to detecting robotic activity of botnets
Prajer, Richard ; Palovský, Radomír (advisor) ; Pavlíček, Luboš (referee)
This thesis explores possible approaches to detecting robotic activity of botnets on network. Initially, the detection based on full packet analysis in consideration of DNS, HTTP and IRC communication, is described. However, this detection is found inapplicable for technical and ethical reasons. Then it focuses on the analysis based on network flow metadata, compiling them to be processable in machine learning. It creates detection models using different machine learning methods, to compare them with each other. Bayes net method is found to be acceptable for detecting robotic activity of botnets. The Bayesian model is only able to identify the botnet that already executes the commands sent by its C&C server. "Sleeping" botnets are not reliably detectable by this model.
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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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