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Made available in DSpace on 2016-06-02T20:06:06Z (GMT). No. of bitstreams: 1 4338.pdf: 1335557 bytes, checksum: 8e0bef5711ff8c398be194e335deecec (MD5) Previous issue date: 2012-02-28 / Universidade Federal de Sao Carlos / Classification consists in the discovery of rules of prediction to assist with planning and decision-making, being a continuously indispensable tool and a highly discussed subject in literature. As a special case in classification, we have the process of credit risk rating, within which there is interest in identifying good and bad paying customers through binary classification methods. Therefore, in many application backgrounds, as in financial, several techniques can be utilized, such as discriminating analysis, probit analysis, logistic regression and neural nets. However, the Probabilistic Nets technique, also known as Bayesian Networks, have showed itself as a practical convenient classification method with successful applications in several areas. In this paper, we aim to display the appliance of Probabilistic Nets in the classification scenario, specifically, the technique named K-dependence Bayesian Networks also known as KDB nets, as well as compared its performance with conventional techniques applied within context of the Credit Scoring and Medical diagnosis. Applications of the technique based in real and artificial datasets and its performance assisted by the bagging procedure will be displayed as results. / A classificação consiste na descoberta de regras de previsão para auxílio no planejamento e tomada de decisões, sendo uma ferramenta indispensável e um tema bastante discutido na literatura. Como caso especial de classificação, temos o processo de avaliação de risco de crédito, no qual temos o interesse de identificar clientes bons e maus pagadores através de métodos de classificação binária. Assim, em diversos enredos de aplicação, como nas financeiras, diversas técnicas podem ser utilizadas, tais como análise discriminante, análise probito, regressão logística e redes neurais. Porém, a técnica de Redes Probabilísticas, também conhecida como Redes Bayesianas, tem se mostrado um método prático de classificação e com aplicações bem sucedidas em diversos campos. Neste trabalho, visamos exibir a aplicação das Redes Probabilísticas no contexto de classificação, em específico, a técnica denominada Redes Probabilísticas com K-dependência, também conhecidas como redes KDB, bem como comparar seu desempenho com as técnicas convencionais aplicadas no contexto de Credit Scoring e Diagnose Médica. Exibiremos como resultado aplicações da técnica baseadas em conjuntos de dados reais e artificiais e seu desempenho auxiliado pelo procedimento de bagging.

Submitted by Priscilla Araujo (priscilla@ibict.br) on 2018-08-07T18:37:30Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tobias Sombra-Mestrado-2018.pdf: 2977663 bytes, checksum: b45309648a3be783327111ae5673abab (MD5) / Made available in DSpace on 2018-08-07T18:37:30Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tobias Sombra-Mestrado-2018.pdf: 2977663 bytes, checksum: b45309648a3be783327111ae5673abab (MD5) Previous issue date: 2018-03-22 / Este trabalho apresenta uma pesquisa exploratória usando o algoritmo Naive Bayes com capacidade para classificar documentos no Mendeley usando até cinco classes de saída, definidas com base na quantidade de leitores dos documentos. Usando uma série de atributos que foram encontrados durante a coleta de dados, é realizada a classificação para tentar identificar padrões nos atributos, a fim de reconhecer lógicas sociais dos cientistas, que envolve tanto o comportamento quanto sua dinâmica nas redes sociais científicas. Para fins de concretização deste trabalho, foi aplicada uma Revisão Sistemática de Literatura, a fim de buscar o estado da arte de pesquisas que envolvam o uso de Reconhecimento de Padrões em Redes Sociais Científicas, além da aplicação de um método que envolve o uso de algoritmos desenvolvidos para o tratamento automático de todos os dados coletados no Mendeley. / This work is an exploratory research using the Naive Bayes algorithm with the ability to classify documents in Mendeley using the output classes, based on the amount of reading of the documents. Using a series of data that was found during a data collection, a classification is given to check the patterns in the attributes, an end to recognize the social logics of the scientists, that involve both the behavior and its dynamics in scientific social networks. For the purpose of this work, a literature systematic review was applied, with emphasis on the use of methods that involve the use of social networking concepts, as well as the application of a method for the use of algorithms. Created for automatic processing of all data collected at Mendeley.

This master's thesis is divided into three parts. In the first part I described P2P lending, its characteristics, basic concepts and practical implications. I also compared P2P market in the Czech Republic, UK and USA. The second part consists of theoretical basics for chosen methods of machine learning, which are naive bayes classifier, classification tree, random forest and logistic regression. I also described methods to evaluate the quality of classification models listed above. The third part is a practical one and shows the complete workflow of creating classification model, from data preparation to evaluation of model.

Terminology is concerned with the creation and maintenance of concept systems, terms and definitions. Automatic term and definition extraction is used to simplify this otherwise manual and sometimes tedious process. This thesis presents an integrated approach of pattern matching and machine learning, utilising feature vectors in which each feature is a Boolean function of a regular expression. The integrated approach is compared with the two more classic approaches, showing a significant increase in recall while maintaining a comparable precision score. Less promising is the negative correlation between the performance of the integrated approach and training size. Further research is suggested.

During the course of this thesis, two scenarios are considered. In the first one, we contribute to feature extraction algorithms. In the second one, we use features to improve object detection solutions and localization. The two scenarios give rise to into four thesis sub-goals. First, we present a new shape skeleton pruning algorithm based on contour approximation and the integer medial axis. The algorithm effectively removes unwanted branches, conserves the connectivity of the skeleton and respects the topological properties of the shape. The algorithm is robust to significant boundary noise and to rigid shape transformations. It is fast and easy to implement. While shape-based solutions via boundary and skeleton analysis are viable solutions to object detection, keypoint features are important for textured object detection. Therefore, we present a keypoint featurebased planar object detection framework for vision-based localization. We demonstrate that our framework is robust against illumination changes, perspective distortion, motion blur, and occlusions. We increase robustness of the localization scheme in cluttered environments and decrease false detection of targets. We present an off-line target evaluation strategy and a scheme to improve pose. Third, we extend planar object detection to a real-time approach for 3D object detection using a mobile and uncalibrated camera. We develop our algorithm based on two novel naive Bayes classifiers for viewpoint and feature matching that improve performance and decrease memory usage. Our algorithm exploits the specific structure of various binary descriptors in order to boost feature matching by conserving descriptor properties. Our novel naive classifiers require a database with a small memory footprint because we only store efficiently encoded features. We improve the feature-indexing scheme to speed up the matching process creating a highly efficient database for objects. Finally, we present a model-free long-term tracking algorithm based on the Kernelized Correlation Filter. The proposed solution improves the correlation tracker based on precision, success, accuracy and robustness while increasing frame rates. We integrate adjustable Gaussian window and sparse features for robust scale estimation creating a better separation of the target and the background. Furthermore, we include fast descriptors and Fourier spectrum packed format to boost performance while decreasing the memory footprint. We compare our algorithm with state-of-the-art techniques to validate the results.

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Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.

This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

This master's thesis deals with data mining techniques and classification of the data into specified categories. The goal of this thesis is to implement a web portal for administration and classification of data from distributed sources. To achieve the goal, it is necessary to test different methods and find the most appropriate one for web articles classification. From the results obtained, there will be developed an automated application for downloading and classification of data from different sources, which would ultimately be able to substitute a user, who would process all the tasks manually.