Dans cette thèse nous étudions l'estimation de modèles probabilistes discriminants, surtout des aspects d'apprentissage semi-supervisé et de sélection de caractéristiques. Le but de l'apprentissage semi-supervisé est d'améliorer l'efficacité de l'apprentissage supervisé en utilisant des données non-étiquetées. Cet objectif est difficile à atteindre dans les cas des modèles discriminants. Les modèles probabilistes discriminants permettent de manipuler des représentations linguistiques riches, sous la forme de vecteurs de caractéristiques de très grande taille. Travailler en grande dimension pose des problèmes, en particulier computationnels, qui sont exacerbés dans le cadre de modèles de séquences tels que les champs aléatoires conditionnels (CRF). Notre contribution est double. Nous introduisons une méthode originale et simple pour intégrer des données non étiquetées dans une fonction objectif semi-supervisée. Nous démontrons alors que l'estimateur semi-supervisé correspondant est asymptotiquement optimal. Le cas de la régression logistique est illustré par des résultats d'expèriences. Dans cette étude, nous proposons un algorithme d'estimation pour les CRF qui réalise une sélection de modèle, par le truchement d'une pénalisation $L_1$. Nous présentons également les résultats d'expériences menées sur des tâches de traitement des langues (le chunking et la détection des entités nommées), en analysant les performances en généralisation et les caractéristiques sélectionnées. Nous proposons finalement diverses pistes pour améliorer l'efficacité computationelle de cette technique.

The extension of Conditional Random Fields (CRF) from discrete states to continuous states will help remove the limitation of the number of states and allow new applications for CRF. In this work, our attempts to obtain a correct procedure to train continuous state conditional random fields through maximum likelihood are presented. By deducing the equations governing the extension of the CRF to continuous states it was possible to merge with the Particle Filter (PF) concept to obtain a formulation governing the training of continuous states CRFs by using particle filters. The results obtained indicated that this process is unsuitable because of the low convergence of the PF integration rate in the needed integrations replacing the summation in CRFs. So a change in concept to an adaptive integration scheme was made. Based on an extension of the Binary Space Partition (BSP) algorithm an adaptive integration process was devised with the aim of producing a more precise integration while retaining a less costly function evaluation than PF. This allowed us to train continuous states conditional random fields with some success. To verify the possibility of increasing the dimension of the states as a vector of continuous states a scalable version was also used to briefly assess its fitness in two-dimensions with quadtrees. This is an asymmetric two-dimensional space partition scheme. In order to increase the knowledge of the problem it would be interesting to have further information of the relevant features. A feature selection embedded method was used based on the lasso regulariser with the intention of pinpointing the most relevant feature functions indicating the relevant features.

Campos Aleatórios Condicionais são modelos probabilísticos discriminativos que tem sido utilizados com sucesso em diversas áreas como processamento de linguagem natural, reconhecimento de fala e bioinformática. Entretanto, implementar algoritmos eficientes para esse tipo de modelo não é uma tarefa fácil. Nesse trabalho apresentamos um arcabouço que ajuda no desenvolvimento e experimentação de Campos Aleatórios Condicionais Semi Markovianos (semi-CRFs). Desenvolvemos algoritmos eficientes que foram implementados em C++ propondo uma interface de programação flexível e intuitiva que habilita o usuário a definir, treinar e avaliar modelos. Nossa implementação foi construída como uma extensão do arcabouço ToPS que, inclusive, pode utilizar qualquer modelo já definido no ToPS como uma função de característica especializada. Por fim utilizamos nossa implementação de semi-CRF para construir um preditor de promotores que apresentou performance superior aos preditores existentes. / Conditional Random Fields are discriminative probabilistic models that have been successfully used in several areas like natural language processing, speech recognition and bioinformatics. However, implementing efficient algorithms for this kind of model is not an easy task. In this thesis we show a framework that helps the development and experimentation of Semi-Markov Conditional Random Fields (semi-CRFs). It has an efficient implementation in C++ and an intuitive API that allow users to define, train and evaluate models. It was built as an extension of ToPS framework and can use ToPS probabilistic models as specialized feature functions. We also use our implementation of semi-CRFs to build a high performance promoter predictor.

Campos Aleatórios Condicionais são modelos probabilísticos discriminativos que tem sido utilizados com sucesso em diversas áreas como processamento de linguagem natural, reconhecimento de fala e bioinformática. Entretanto, implementar algoritmos eficientes para esse tipo de modelo não é uma tarefa fácil. Nesse trabalho apresentamos um arcabouço que ajuda no desenvolvimento e experimentação de Campos Aleatórios Condicionais Semi Markovianos (semi-CRFs). Desenvolvemos algoritmos eficientes que foram implementados em C++ propondo uma interface de programação flexível e intuitiva que habilita o usuário a definir, treinar e avaliar modelos. Nossa implementação foi construída como uma extensão do arcabouço ToPS que, inclusive, pode utilizar qualquer modelo já definido no ToPS como uma função de característica especializada. Por fim utilizamos nossa implementação de semi-CRF para construir um preditor de promotores que apresentou performance superior aos preditores existentes. / Conditional Random Fields are discriminative probabilistic models that have been successfully used in several areas like natural language processing, speech recognition and bioinformatics. However, implementing efficient algorithms for this kind of model is not an easy task. In this thesis we show a framework that helps the development and experimentation of Semi-Markov Conditional Random Fields (semi-CRFs). It has an efficient implementation in C++ and an intuitive API that allow users to define, train and evaluate models. It was built as an extension of ToPS framework and can use ToPS probabilistic models as specialized feature functions. We also use our implementation of semi-CRFs to build a high performance promoter predictor.

A escolha da conferência adequada para o envio de um artigo é uma tarefa que depende de diversos fatores: (i) o tema do trabalho deve estar entre os temas de interesse do evento; (ii) o prazo de submissão do evento deve ser compatível com tempo necessário para a escrita do artigo; (iii) localização da conferência e valores de inscrição são levados em consideração; e (iv) a qualidade da conferência (Qualis) avaliada pela CAPES. Esses fatores aliados à existência de milhares de conferências tornam a busca pelo evento adequado bastante demorada, em especial quando se está pesquisando em uma área nova. A fim de auxiliar os pesquisadores na busca de conferências, o trabalho aqui desenvolvido apresenta um método para a coleta e extração de dados de sites de conferências. Essa é uma tarefa desafiadora, principalmente porque cada conferência possui seu próprio site, com diferentes layouts. O presente trabalho apresenta um método chamado CONFTRACKER que combina a identificação de URLs de conferências da Tabela Qualis à identificação de deadlines a partir de seus sites. A extração das informações é realizada independente da conferência, do layout do site e da forma como são apresentadas as datas (formatação e rótulos). Para avaliar o método proposto, foram realizados experimentos com dados reais de conferências da Ciência da Computação. Os resultados mostraram que CONFTRACKER obteve resultados significativamente melhores em relação a um baseline baseado na posição entre rótulos e datas. Por fim, o processo de extração é executado para todas as conferências da Tabela Qualis e os dados coletados populam uma base de dados que pode ser consultada através de uma interface online. / Choosing the most suitable conference to submit a paper is a task that depends on various factors: (i) the topic of the paper needs to be among the topics of interest of the conference; (ii) submission deadlines need to be compatible with the necessary time for paper writing; (iii) conference location and registration costs; and (iv) the quality or impact of the conference. These factors allied to the existence of thousands of conferences, make the search of the right event very time consuming, especially when researching in a new area. Intending to help researchers finding conferences, this work presents a method developed to retrieve and extract data from conference web sites. Our method combines the identification of conference URL and deadline extraction. This is a challenging task as each web site has its own layout. Here, we propose CONFTRACKER, which combines the identification of the URLs of conferences listed in the Qualis Table and the extraction of their deadlines. Information extraction is carried out independent from the page’s layout and how the dates are presented. To evaluate our proposed method, we carried out experiments with real web data from Computer Science conferences. The results show that CONFTRACKER outperformed a baseline method based on the position of labels and dates. Finaly, the extracted data is stored in a database to be searched with an online tool.

Intrusion Detection systems are now an essential component in the overall network and data security arsenal. With the rapid advancement in the network technologies including higher bandwidths and ease of connectivity of wireless and mobile devices, the focus of intrusion detection has shifted from simple signature matching approaches to detecting attacks based on analyzing contextual information which may be specific to individual networks and applications. As a result, anomaly and hybrid intrusion detection approaches have gained significance. However, present anomaly and hybrid detection approaches suffer from three major setbacks; limited attack detection coverage, large number of false alarms and inefficiency in operation. / In this thesis, we address these three issues by introducing efficient intrusion detection frameworks and models which are effective in detecting a wide variety of attacks and which result in very few false alarms. Additionally, using our approach, attacks can not only be accurately detected but can also be identified which helps to initiate effective intrusion response mechanisms in real-time. Experimental results performed on the benchmark KDD 1999 data set and two additional data sets collected locally confirm that layered conditional random fields are particularly well suited to detect attacks at the network level and user session modeling using conditional random fields can effectively detect attacks at the application level. / We first introduce the layered framework with conditional random fields as the core intrusion detector. Layered conditional random field can be used to build scalable and efficient network intrusion detection systems which are highly accurate in attack detection. We show that our systems can operate either at the network level or at the application level and perform better than other well known approaches for intrusion detection. Experimental results further demonstrate that our system is robust to noise in training data and handles noise better than other systems such as the decision trees and the naive Bayes. We then introduce our unified logging framework for audit data collection and perform user session modeling using conditional random fields to build real-time application intrusion detection systems. We demonstrate that our system can effectively detect attacks even when they are disguised within normal events in a single user session. Using our user session modeling approach based on conditional random fields also results in early attack detection. This is desirable since intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of an attack.

A escolha da conferência adequada para o envio de um artigo é uma tarefa que depende de diversos fatores: (i) o tema do trabalho deve estar entre os temas de interesse do evento; (ii) o prazo de submissão do evento deve ser compatível com tempo necessário para a escrita do artigo; (iii) localização da conferência e valores de inscrição são levados em consideração; e (iv) a qualidade da conferência (Qualis) avaliada pela CAPES. Esses fatores aliados à existência de milhares de conferências tornam a busca pelo evento adequado bastante demorada, em especial quando se está pesquisando em uma área nova. A fim de auxiliar os pesquisadores na busca de conferências, o trabalho aqui desenvolvido apresenta um método para a coleta e extração de dados de sites de conferências. Essa é uma tarefa desafiadora, principalmente porque cada conferência possui seu próprio site, com diferentes layouts. O presente trabalho apresenta um método chamado CONFTRACKER que combina a identificação de URLs de conferências da Tabela Qualis à identificação de deadlines a partir de seus sites. A extração das informações é realizada independente da conferência, do layout do site e da forma como são apresentadas as datas (formatação e rótulos). Para avaliar o método proposto, foram realizados experimentos com dados reais de conferências da Ciência da Computação. Os resultados mostraram que CONFTRACKER obteve resultados significativamente melhores em relação a um baseline baseado na posição entre rótulos e datas. Por fim, o processo de extração é executado para todas as conferências da Tabela Qualis e os dados coletados populam uma base de dados que pode ser consultada através de uma interface online. / Choosing the most suitable conference to submit a paper is a task that depends on various factors: (i) the topic of the paper needs to be among the topics of interest of the conference; (ii) submission deadlines need to be compatible with the necessary time for paper writing; (iii) conference location and registration costs; and (iv) the quality or impact of the conference. These factors allied to the existence of thousands of conferences, make the search of the right event very time consuming, especially when researching in a new area. Intending to help researchers finding conferences, this work presents a method developed to retrieve and extract data from conference web sites. Our method combines the identification of conference URL and deadline extraction. This is a challenging task as each web site has its own layout. Here, we propose CONFTRACKER, which combines the identification of the URLs of conferences listed in the Qualis Table and the extraction of their deadlines. Information extraction is carried out independent from the page’s layout and how the dates are presented. To evaluate our proposed method, we carried out experiments with real web data from Computer Science conferences. The results show that CONFTRACKER outperformed a baseline method based on the position of labels and dates. Finaly, the extracted data is stored in a database to be searched with an online tool.

A escolha da conferência adequada para o envio de um artigo é uma tarefa que depende de diversos fatores: (i) o tema do trabalho deve estar entre os temas de interesse do evento; (ii) o prazo de submissão do evento deve ser compatível com tempo necessário para a escrita do artigo; (iii) localização da conferência e valores de inscrição são levados em consideração; e (iv) a qualidade da conferência (Qualis) avaliada pela CAPES. Esses fatores aliados à existência de milhares de conferências tornam a busca pelo evento adequado bastante demorada, em especial quando se está pesquisando em uma área nova. A fim de auxiliar os pesquisadores na busca de conferências, o trabalho aqui desenvolvido apresenta um método para a coleta e extração de dados de sites de conferências. Essa é uma tarefa desafiadora, principalmente porque cada conferência possui seu próprio site, com diferentes layouts. O presente trabalho apresenta um método chamado CONFTRACKER que combina a identificação de URLs de conferências da Tabela Qualis à identificação de deadlines a partir de seus sites. A extração das informações é realizada independente da conferência, do layout do site e da forma como são apresentadas as datas (formatação e rótulos). Para avaliar o método proposto, foram realizados experimentos com dados reais de conferências da Ciência da Computação. Os resultados mostraram que CONFTRACKER obteve resultados significativamente melhores em relação a um baseline baseado na posição entre rótulos e datas. Por fim, o processo de extração é executado para todas as conferências da Tabela Qualis e os dados coletados populam uma base de dados que pode ser consultada através de uma interface online. / Choosing the most suitable conference to submit a paper is a task that depends on various factors: (i) the topic of the paper needs to be among the topics of interest of the conference; (ii) submission deadlines need to be compatible with the necessary time for paper writing; (iii) conference location and registration costs; and (iv) the quality or impact of the conference. These factors allied to the existence of thousands of conferences, make the search of the right event very time consuming, especially when researching in a new area. Intending to help researchers finding conferences, this work presents a method developed to retrieve and extract data from conference web sites. Our method combines the identification of conference URL and deadline extraction. This is a challenging task as each web site has its own layout. Here, we propose CONFTRACKER, which combines the identification of the URLs of conferences listed in the Qualis Table and the extraction of their deadlines. Information extraction is carried out independent from the page’s layout and how the dates are presented. To evaluate our proposed method, we carried out experiments with real web data from Computer Science conferences. The results show that CONFTRACKER outperformed a baseline method based on the position of labels and dates. Finaly, the extracted data is stored in a database to be searched with an online tool.

This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.

Ce travail présente une étude linguistique des expressions d'opinions issues de différentes sources dans des textes en espagnol. Le travail comprend la définition d'un modèle pour les prédicats d'opinion et leurs arguments (la source, le sujet et le message), la création d'un lexique de prédicats d'opinions auxquels sont associées des informations provenant du modèle et la réalisation de trois systèmes informatiques.Le premier système, basé sur des règles contextuelles, obtient de bons résultats pour le score de F-mesure partielle: prédicat, 92%; source, 81%; sujet, 75%; message, 89%, opinion, 85%. En outre, l'identification de la source donne une valeur de 79% de F-mesure exacte. Le deuxième système, basé sur le modèle Conditional Random Fields (CRF), a été développé uniquement pour l'identification des sources, donnant une valeur de 76% de F-mesure exacte. Le troisième système, qui combine les deux techniques (règles et CRF), donne une valeur de 83% de F-mesure exacte, montrant ainsi que la combinaison permet d'obtenir des résultats intéressants.En ce qui concerne l'identification des sources, notre système, comparé à des travaux réalisés sur des corpus d'autres langues que l'espagnol, donne des résultats très satisfaisants. En effet ces différents travaux obtiennent des scores qui se situent entre 63% et 89,5%.Par ailleurs, en sus des systèmes réalisés pour l'identification de l'opinion, notre travail a débouché sur la construction de plusieurs ressources pour l'espagnol : un lexique de prédicats d'opinions, un corpus de 13000 mots avec des annotations sur les opinions et un corpus de 40000 mots avec des annotations sur les prédicats d'opinion et les sources. / This work presents a study of linguistic expressions of opinion from different sources in Spanish texts. The work includes the definition of a model for opinion predicates and their arguments (source, topic and message), the creation of a lexicon of opinion predicates which have information from the model associated, and the implementation of three systems.The first system, based on contextual rules, gets good results for the F-measure score (partial match): predicate, 92%; source, 81%; topic, 75%; message, 89%; full opinion, 85%. In addition, for source identification the F-measure for exact match is 79%. The second system, based on Conditional Random Fields (CRF), was developed only for the identification of sources, giving 76% of F-measure (exact match). The third system, which combines the two techniques (rules and CRF), gives a value of 83% of F-measure (exact match), showing that the combination yields interesting results.As regards the identification of sources, our system compared to other work developed for languages ​other than Spanish, gives very satisfactory results. Indeed these works had scores that fall between 63% and 89.5%.Moreover, in addition to the systems made for the identification of opinions, our work has led to the construction of several resources for Spanish: a lexicon of opinion predicates, a 13,000 words corpus with opinions annotated and a 40,000 words corpus with opinion predicates end sources annotated.