Interactive classi&amp / #64257 / cation is an attractive alternative and complementary for automatic classi&amp / #64257 / cation of satellite image content, since the subject is visual and there are not yet powerful computational features corresponding to the sought visual features. In this study, we improve our previous attempt by building a more stable software system with better capabilities for interactive classi&amp / #64257 / cation of the content of satellite images. The system allows user to indicate a few number of image regions that contain a speci&amp / #64257 / c geographical object, for example, a bridge, and to retrieve similar objects on the same satellite images. Retrieval process is iterative in the sense that user guides the classi&amp / #64257 / cation procedure by interaction and visual observation of the results. The classi&amp / #64257 / cation procedure is based on one-class classi&amp / #64257 / cation.

Made available in DSpace on 2014-12-17T14:56:21Z (GMT). No. of bitstreams: 1 DaniloLS.pdf: 2499617 bytes, checksum: 328b5ce6d56f5a92a61ad220565411c7 (MD5) Previous issue date: 2006-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents / Derramamentos de ?leo sobre o mar, mesmo que acidentais, geram enormes conseq??ncias negativas para a ?rea afetada. Os preju?zos s?o ambientais e econ?micos, principalmente com a proximidade dessas manchas de ?reas de preserva??o e/ou zonas costeiras. O desenvolvimento de t?cnicas autom?ticas para a identifica??o de manchas de ?leo sobre a superf?cie marinha, capturadas atrav?s de imagens de Radar, auxiliam num completo monitoramento dos oceanos e mares. Contudo, manchas de diferentes origens podem ser visualizadas nesse tipo de produ??o de imagem, tornando o monitoramento dif?cil. O sistema proposto neste trabalho, baseado em t?cnicas de processamento digital de imagens e redes neurais artificiais, tem o objetivo de identificar a mancha analisada e discernir entre ?leo e os demais fen?menos geradores de mancha. Testes nos blocos funcionais que comp?em o sistema proposto permitem a implementa??o de diferentes algoritmos, assim como sua an?lise detalhada e pontual. Os algoritmos que tratam do processamento digital de imagem (filtragem do ru?do speckle e gradiente), assim como o de classifica??o (Perceptron de M?ltiplas Camadas, rede de fun??o de Base Radial, M?quina de Vetor de Suporte e M?quina de comit?) s?o apresentados e comentados.O desempenho final do sistema, com diferentes tipos de classificadores, ? apresentado atrav?s da curva ROC. As taxas de acertos s?o consideradas condizentes com o que a literatura de detec??o de manchas de ?leo na superf?cie oce?nica atrav?s de imagens de SAR apresenta

The aim of the present study is to identify the di®erentially expressed genes be- tween two di®erent conditions and apply it in predicting the class of new samples using the microarray data. Microarray data analysis poses many challenges to the statis- ticians because of its high dimensionality and small sample size, dubbed as "small n large p problem". Microarray data has been extensively studied by many statisticians and geneticists. Generally, it is said to follow a normal distribution with equal vari- ances in two conditions, but it is not true in general. Since the number of replications is very small, the sample estimates of variances are not appropriate for the testing. Therefore, we have to consider the Bayesian approach to approximate the variances in two conditions. Because the number of genes to be tested is usually large and the test is to be repeated thousands of times, there is a multiplicity problem. To remove the defect arising from multiple comparison, we use the False Discovery Rate (FDR) correction. Applying the hypothesis test repeatedly gene by gene for several thousands of genes, there is a great chance of selecting false genes as di®erentially expressed, even though the signi¯cance level is set very small. For the test to be reliable, the probability of selecting true positive should be high. To control the false positive rate, we have applied the FDR correction, in which the p -values for each of the gene is compared with its corresponding threshold. A gene is, then, said to be di®erentially expressed if the p-value is less than the threshold. We have developed a new method of selecting informative genes based on the Bayesian Version of Behrens-Fisher distribution which assumes the unequal variances in two conditions. Since the assumption of equal variances fail in most of the situation and the equal variance is a special case of unequal variance, we have tried to solve the problem of ¯nding di®erentially expressed genes in the unequal variance cases. We have found that the developed method selects the actual expressed genes in the simulated data and compared this method with the recent methods such as Fox and Dimmic’s t-test method, Tusher and Tibshirani’s SAM method among others. The next step of this research is to check whether the genes selected by the pro- posed Behrens -Fisher method is useful for the classi¯cation of samples. Using the genes selected by the proposed method that combines the Behrens Fisher gene se- lection method with some other statistical learning methods, we have found better classi¯cation result. The reason behind it is the capability of selecting the genes based on the knowledge of prior and data. In the case of microarray data due to the small sample size and the large number of variables, the variances obtained by the sample is not reliable in the sense that it is not positive de¯nite and not invertible. So, we have derived the Bayesian version of the Behrens Fisher distribution to remove that insu±ciency. The e±ciency of this established method has been demonstrated by ap- plying them in three real microarray data and calculating the misclassi¯cation error rates on the corresponding test sets. Moreover, we have compared our result with some of the other popular methods, such as Nearest Shrunken Centroid and Support Vector Machines method, found in the literature. We have studied the classi¯cation performance of di®erent classi¯ers before and after taking the correlation between the genes. The classi¯cation performance of the classi¯er has been signi¯cantly improved once the correlation was accounted. The classi¯cation performance of di®erent classi¯ers have been measured by the misclas- si¯cation rates and the confusion matrix. The another problem in the multiple testing of large number of hypothesis is the correlation among the test statistics. we have taken the correlation between the test statistics into account. If there were no correlation, then it will not a®ect the shape of the normalized histogram of the test statistics. As shown by Efron, the degree of the correlation among the test statistics either widens or shrinks the tail of the histogram of the test statistics. Thus the usual rejection region as obtained by the signi¯cance level is not su±cient. The rejection region should be rede¯ned accordingly and depends on the degree of correlation. The e®ect of the correlation in selecting the appropriate rejection region have also been studied.

Nesta tese, aborda-se o problema de classificaÃÃo de dados que estÃo contaminados com pa- drÃes atÃpicos. Tais padrÃes, genericamente chamados de outliers, sÃo onipresentes em conjunto de dados multivariados reais, porÃm sua detecÃÃo a priori (i.e antes de treinar um classificador) à uma tarefa de difÃcil realizaÃÃo. Como conseqÃÃncia, uma abordagem reativa, em que se desconfia da presenÃa de outliers somente apÃs um classificador previamente treinado apresen- tar baixo desempenho, à a mais comum. VÃrias estratÃgias podem entÃo ser levadas a cabo a fim de melhorar o desempenho do classificador, dentre elas escolher um classificador mais poderoso computacionalmente ou promover uma limpeza dos dados, eliminando aqueles pa- drÃes difÃceis de categorizar corretamente. Qualquer que seja a estratÃgia adotada, a presenÃa de outliers sempre irà requerer maior atenÃÃo e cuidado durante o projeto de um classificador de padrÃes. Tendo estas dificuldades em mente, nesta tese sÃo revisitados conceitos e tÃcni- cas provenientes da teoria de regressÃo robusta, em particular aqueles relacionados à estimaÃÃo M, adaptando-os ao projeto de classificadores de padrÃes capazes de lidar automaticamente com outliers. Esta adaptaÃÃo leva à proposiÃÃo de versÃes robustas de dois classificadores de padrÃes amplamente utilizados na literatura, a saber, o classificador linear dos mÃnimos qua- drados (least squares classifier, LSC) e a mÃquina de aprendizado extremo (extreme learning machine, ELM). AtravÃs de uma ampla gama de experimentos computacionais, usando dados sintÃticos e reais, mostra-se que as versÃes robustas dos classificadores supracitados apresentam desempenho consistentemente superior aos das versÃes originais. / This thesis addresses the problem of data classification when they are contaminated with atypical patterns. These patterns, generally called outliers, are omnipresent in real-world multi- variate data sets, but their a priori detection (i.e. before training the classifier) is a difficult task to perform. As a result, the most common approach is the reactive one, in which one suspects of the presence of outliers in the data only after a previously trained classifier has achieved a low performance. Several strategies can then be carried out to improve the performance of the classifier, such as to choose a more computationally powerful classifier and/or to remove the de- tected outliers from data, eliminating those patterns which are difficult to categorize properly. Whatever the strategy adopted, the presence of outliers will always require more attention and care during the design of a pattern classifier. Bearing these difficulties in mind, this thesis revi- sits concepts and techniques from the theory of robust regression, in particular those related to M-estimation, adapting them to the design of pattern classifiers which are able to automatically handle outliers. This adaptation leads to the proposal of robust versions of two pattern classi- fiers widely used in the literature, namely, least squares classifier (LSC) and extreme learning machine (ELM). Through a comprehensive set of computer experiments using synthetic and real-world data, it is shown that the proposed robust classifiers consistently outperform their original versions.

No description available.

Ensemble learning is a multiple-classi&amp / #64257 / er machine learning approach which combines, produces collections and ensembles statistical classi&amp / #64257 / ers to build up more accurate classi&amp / #64257 / er than the individual classi&amp / #64257 / ers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this thesis, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed systems &amp / #64257 / nd an elegant way of boosting a bunch of classi&amp / #64257 / ers successively to form a better classi&amp / #64257 / er than each ensembled classi&amp / #64257 / er. AdaBoost algorithm employs a greedy search over hypothesis space to &amp / #64257 / nd a good suboptimal solution. On the other hand, this work proposes an evolutionary search with genetic algorithms instead of greedy search. Empirical results show that classi&amp / #64257 / cation with boosted evolutionary computing outperforms AdaBoost in equivalent experimental environments.

Les intérêts théoriques, cliniques et économiques, de l'informatisation des prescriptions au sein des établissements de santé sont nombreux : diminution du nombre de prescriptions, amélioration de leur pertinence clinique, diminution des erreurs médicales... Ces béné ces restent théoriques car l'informatisation des prescriptions est, en pratique, confrontée à de nombreux problèmes, parmi lesquels les problèmes d'interopérabilité et d'utilisabilité des solutions logicielles. L'utilisation de terminologies d'interface au sein de ux de terminologies permettrait de dépasser ces problèmes. L'objectif principal de ce travail était de modéliser et développer ces ux de terminologies pour la production d'examens de biologie et d'imagerie médicale puis d'en évaluer les béné ces en termes d'interopérabilité et d'utilisabilité. Des techniques d'analyse des processus ont permis d'aboutir à une modélisation des ux de terminologies qui semble commune à de nombreux domaines. La création des ux proprement dits repose sur des terminologies d'interface, éditées pour l'occasion, et des référentiels nationaux ou internationaux reconnus. Pour l'évaluation, des méthodes spéci- ques mises au point lors du travail d'intégration d'une terminologie d'interface iconique au sein d'un moteur de recherche de recommandations médicales et d'un dossier médical, ont été appliquées. Les ux de terminologies créés induisaient d'importantes pertes d'information entre les di érents systèmes d'information. En imagerie, la terminologie d'interface de prescription était signi cativement plus simple à utiliser que les autres terminologies, une telle di érence n'a pas été mise en évidence dans le domaine de la biologie. Si les ux de terminologies ne sont pas encore fonctionnels, les terminologies d'interface, elles, sont disponibles pour tout établissement de santé ou éditeur de logiciels et devraient faciliter la mise en place de logiciels d'aide à la prescription.

Made available in DSpace on 2014-12-17T15:48:05Z (GMT). No. of bitstreams: 1 MarjoryCCA.pdf: 917121 bytes, checksum: 918ccb19adcf29ebd6cdbf1f3ac97310 (MD5) Previous issue date: 2006-10-26 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The use of multi-agent systems for classification tasks has been proposed in order to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems. As a result, the NeurAge system was proposed. This system is composed by several neural agents which communicate and negotiate a common result for the testing patterns. In the NeurAge system, a negotiation method is very important to the overall performance of the system since the agents need to reach and agreement about a problem when there is a conflict among the agents. This thesis presents an extensive analysis of the NeurAge System where it is used all kind of classifiers. This systems is now named ClassAge System. It is aimed to analyze the reaction of this system to some modifications in its topology and configuration / A utiliza??o de sistemas baseados no paradigma dos agentes para resolu??o de problemas de reconhecimento de padr?es vem sendo propostos com o intuito de resolver, ou atenuar, o problema de tomada de decis?o centralizada dos sistemas multi-classificadores e, como consequ?ncia, melhorar sua capacidade de classifica??o. Com a inten??o de solucionar este problema, o Sistema NeurAge foi proposto. Este sistema ? composto por agentes neurais que podem se comunicar e negociar um resultado comum para padr?es de teste. No Sistema NeurAge, os m?todos de negocia??o s?o muito importantes para prover uma melhor precis?o ao sistema, pois os agentes necessitam alcan?ar a melhor solu??o e resolver conflitos, quando estes existem, em rela??o a um problema. Esta disserta??o apresenta uma extens?o do Sistema NeurAge que pode utilizar qualquer tipo de classificador e agora ser? chamado de Sistema ClassAge. Aqui ? feita uma an?lise do comportamento do Sistema ClassAge diante de v?rias modifica??es na topologia e nas configura??es dos componentes deste sistema

Our sample (Xit; Yit) consists of pairs of variables. The real variable Xit measures the creditworthiness of individual i in period t. The Bernoulli variable Yit is the default indicator of individual i in period t. The objective is to estimate a credit rating system, i.e. to particularly divide the range of the creditworthiness into several rating classes, each with a homogeneous default risk. The field of change point analysis provides a way to estimate the breakpoints between the rating classes. As yet, the literature only considers models without dependencies or with dependence only in cross section. This contribution proposes multi-period models including dependencies in cross section as well as in longitudinal section. Furthermore, estimators for the model parameters are suggested. The estimators are applied to a data set of a German credit bureau.

La tesi mette a confronto l'universo delle riorganizzazioni nel Chapter 11 con i nuovi modelli di ristrutturazione concessi dalla riforma del diritto fallimentare. In modo particolare la tesi si sofferma sul mercato del controllo nella crisi di impresa. Negli Stati Uniti c'e' da tempo un mercato dei crediti sofferenti, mentre in Italia scontiamo ancora i ritardi del sistema economico. Il primo capitolo racconta i temi collegati al mercato del controllo nel Chapter 11: gli acquisti dei crediti nelle diverse classi creditorie, la nuova finanza concessa al debtor in possession, il controllo da covenant, la remunerazione degli amministratori con il debito, i derivati sul credito e il voto connesso. Il secondo capitolo si sofferma sull'interpretazione degli artt. 124 e 127 della legge fallimentare letti nell'ottica di un potenziale mercato del controllo nella crisi di impresa come nel caso del concordato con assunzione e si interroga infine sull'esenzione o meno da opa obbligatoria di tali operazioni alla luce dell'art. 106 TUF. / The thesis compares the world of Chapter 11 reorganizations with the new types of reorganizations introduced in Italy by the recent reform of bankruptcy law. In particular the thesis deals with the market for corporate control in the insolvency arena in both countries. In the States bankruptcy claims are traded on a regular basis whereas Italy still hasn't fully experienced transfers of control within the frame of a corporate reorganization. The first chapter focuses on all issues connected to US M&A in bankruptcy: acquisition of claims in the different classes, control rights in covenants, debtor-in-possession financing, pay for performance in bankruptcy, credit default swaps and empty voting. The second chapter focuses on the interpretation of articles 124 and 127 of the new Italian bankruptcy law which may lead to the creation of a market for corporate control within the frame of a composition with a third party buyer and discusses the potential applicability of mandatory bids pursuant to art. 106 TUF to such deals.