This paper presents an approach to model selection for regularized least-squares on reproducing kernel Hilbert spaces in the semi-supervised setting. The role of effective dimension was recently shown to be crucial in the definition of a rule for the choice of the regularization parameter, attaining asymptotic optimal performances in a minimax sense. The main goal of the present paper is showing how the effective dimension can be replaced by an empirical counterpart while conserving optimality. The empirical effective dimension can be computed from independent unlabelled samples. This makes the approach particularly appealing in the semi-supervised setting.

The Dr. Peter Centre (DPC), an HIV care facility, provides integrated health care services incorporating harm reduction strategies as part of service provision. These services include a “Harm Reduction Room” for those members who inject drugs, to do so in a supervised environment. In this thesis, I explore the perspectives of DPC members on the harm reduction approach as part of a larger study titled A mixed Method Evaluation of the Impact of the Dr. Peter Centre on Health Care Access and Outcomes for Persons Living with HIV/AIDS who use Illicit Drugs. Thirty DPC members were recruited as part of the qualitative portion of the larger mixed-method study. One-on-one in depth interviews were conducted with each participant and audio-recorded and then transcribed verbatim. Participant narratives reflected positive experiences with nurses and other staff, and with the harm reduction philosophy at the DPC. Narratives from both participants who inject drugs and participants who do not inject drugs indicated support for the harm reduction room because of the safety it provides. Safety was related to reducing the direct harmful effects of injection drugs such as infection and overdose, and also to the refuge from the street and freedom from stigma of drug use that the DPC provides. Participant accounts expressed a sense of acceptance and belonging as a part of the community at the DPC highlighting the role of DPC in shifting drug use patterns. This thesis emphasizes that the harm reduction philosophy and the provision of harm reduction services at the DPC contributes to the overall health and well being of participants. / Graduate

Today, a large amount of raw data are available within manufacturing industries. Unfortunately, most of it is not further analyzed in search of valuable information regarding the optimization of processes. In the painting process at the Volvo plant in Umeå, adjusted settings on the process equipments (e.g. robots, machines etc.) are mostly based on the experience of the personnel rather than actual facts (i.e. analyzed data). Consequently, time- and cost waste caused by defects is obtained when painting the commercial heavy-duty truck bodies (cabs). Hence, the aim of this masters thesis is to model the quality as a function of available background- and process data. This should be presented in an automated alarm and root-cause system. A variety of supervised learning algorithms were trained in order to estimate the probability of having at least one defect per cab. Even with a small amount of data, results have shown that such algorithms can provide valuable information. Later in this thesis work, one of the algorithms was chosen and used as the underlying model in the prototype of an automated alarm system. When this probability was considered as too high, an intuitive root-cause analysis was presented. Ultimately, this research has demonstrated the importance and possibility of analyzing data with statistical tools in the search of limiting costs- and time waste.

Orientador: Silvia Regina Quijadas Aro Zuliani / Banca: Iramaia Jorge Cabral de Paulo / Banca: José Guilherme da Silva Lopes / Banca: Antonio Francisco Marques / Banca: Marcelo Carbone Carneiro / Resumo: Uma estratégia de ensino que favoreça a investigação pelos alunos, oportunizando o conhecimento científico, tem sido estudada por autores para proporcionar a oportunidade de discussões acerca dos temas e fenômenos em estudo. Considerando o ensino por investigação como uma estratégia capaz de levar os alunos ao debate, estimulando a discussão e a argumentação, a presente pesquisa analisou se esta estratégia de ensino oportuniza uma aprendizagem significativa critica dos alunos de um colégio público federal de Minas Gerais, se aproximando dos princípios da Teoria da Aprendizagem Significativa Crítica. Também investigou qual o papel do professor em uma investigação orientada e de que maneiras esses sujeitos expressam essa experiência relacionando-se ao seu cotidiano. Para a análise dos dados utilizou-se a fenomenologia para captar a expressão dos sujeitos envolvidos no processo e elencar as unidades de significado, para obter possíveis pontes de convergências entre o ensino por investigação e os princípios da Teoria da Aprendizagem Significativa Crítica. Os resultados mostram que esta é uma estratégia de ensino pouco utilizada pelos professores, apesar de favorecer um estreitamento entre a realidade dos alunos e os conceitos científicos, oportunizar a discussão e a formulação de hipóteses, justificar os fenômenos estudados, além de mostrar o papel do professor como orientador e estimulador na aquisição do conhecimento pretendido e de outros conceitos envolvidos para a explicação do fato / Abstract: A teaching strategy that encourages inquiry by students, providing opportunities for scientific knowledge, has been studied by the authors to provide an opportunity for discussions about the issues and phenomena under study. Considering inquiry in science education as a strategy can lead students to debate, stimulating discussion and argument, the present study examined whether his strategy of teaching nurture a critical meaningful learning of students of a public school Federal de Minas Gerais, approaching principles of Critical Theory Of Meaningful Learning . We also investigated the role of the teacher in a oriented research and the ways in which these subjects express this experience relating it to their daily lives. For data analysis we used phenomenology to capture the expression of the subjects involved in the process and list the signified units, for possible bridges convergences between Inquiry in science education by the principles of Critical Theory of Meaningful Learning. The results show that this is a teaching strategy used by some teachers, although favoring a narrowing between the reality of students and scientific concepts, make the discussion and formulation of hypotheses, justify the phenomena studied, and show the role of the teacher as leadera and stimulating the aquisition of knowledge required and other concepts involved in explaining the fact / Doutor

Made available in DSpace on 2014-06-11T19:31:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-10-16Bitstream added on 2014-06-13T19:20:58Z : No. of bitstreams: 1 vieira_fac_dr_bauru.pdf: 904329 bytes, checksum: d9a0841391e12cc7d8df2b97fc47d5b0 (MD5) / Proquali / Uma estratégia de ensino que favoreça a investigação pelos alunos, oportunizando o conhecimento científico, tem sido estudada por autores para proporcionar a oportunidade de discussões acerca dos temas e fenômenos em estudo. Considerando o ensino por investigação como uma estratégia capaz de levar os alunos ao debate, estimulando a discussão e a argumentação, a presente pesquisa analisou se esta estratégia de ensino oportuniza uma aprendizagem significativa critica dos alunos de um colégio público federal de Minas Gerais, se aproximando dos princípios da Teoria da Aprendizagem Significativa Crítica. Também investigou qual o papel do professor em uma investigação orientada e de que maneiras esses sujeitos expressam essa experiência relacionando-se ao seu cotidiano. Para a análise dos dados utilizou-se a fenomenologia para captar a expressão dos sujeitos envolvidos no processo e elencar as unidades de significado, para obter possíveis pontes de convergências entre o ensino por investigação e os princípios da Teoria da Aprendizagem Significativa Crítica. Os resultados mostram que esta é uma estratégia de ensino pouco utilizada pelos professores, apesar de favorecer um estreitamento entre a realidade dos alunos e os conceitos científicos, oportunizar a discussão e a formulação de hipóteses, justificar os fenômenos estudados, além de mostrar o papel do professor como orientador e estimulador na aquisição do conhecimento pretendido e de outros conceitos envolvidos para a explicação do fato / A teaching strategy that encourages inquiry by students, providing opportunities for scientific knowledge, has been studied by the authors to provide an opportunity for discussions about the issues and phenomena under study. Considering inquiry in science education as a strategy can lead students to debate, stimulating discussion and argument, the present study examined whether his strategy of teaching nurture a critical meaningful learning of students of a public school Federal de Minas Gerais, approaching principles of Critical Theory Of Meaningful Learning . We also investigated the role of the teacher in a oriented research and the ways in which these subjects express this experience relating it to their daily lives. For data analysis we used phenomenology to capture the expression of the subjects involved in the process and list the signified units, for possible bridges convergences between Inquiry in science education by the principles of Critical Theory of Meaningful Learning. The results show that this is a teaching strategy used by some teachers, although favoring a narrowing between the reality of students and scientific concepts, make the discussion and formulation of hypotheses, justify the phenomena studied, and show the role of the teacher as leadera and stimulating the aquisition of knowledge required and other concepts involved in explaining the fact

abstract: The rapid growth in the high-throughput technologies last few decades makes the manual processing of the generated data to be impracticable. Even worse, the machine learning and data mining techniques seemed to be paralyzed against these massive datasets. High-dimensionality is one of the most common challenges for machine learning and data mining tasks. Feature selection aims to reduce dimensionality by selecting a small subset of the features that perform at least as good as the full feature set. Generally, the learning performance, e.g. classification accuracy, and algorithm complexity are used to measure the quality of the algorithm. Recently, the stability of feature selection algorithms has gained an increasing attention as a new indicator due to the necessity to select similar subsets of features each time when the algorithm is run on the same dataset even in the presence of a small amount of perturbation. In order to cure the selection stability issue, we should understand the cause of instability first. In this dissertation, we will investigate the causes of instability in high-dimensional datasets using well-known feature selection algorithms. As a result, we found that the stability mostly data-dependent. According to these findings, we propose a framework to improve selection stability by solving these main causes. In particular, we found that data noise greatly impacts the stability and the learning performance as well. So, we proposed to reduce it in order to improve both selection stability and learning performance. However, current noise reduction approaches are not able to distinguish between data noise and variation in samples from different classes. For this reason, we overcome this limitation by using Supervised noise reduction via Low Rank Matrix Approximation, SLRMA for short. The proposed framework has proved to be successful on different types of datasets with high-dimensionality, such as microarrays and images datasets. However, this framework cannot handle unlabeled, hence, we propose Local SVD to overcome this limitation. / Dissertation/Thesis / Ph.D. Computer Science 2013

L'apprentissage actif apparaît comme un problème important dans différents contextes de l'apprentissage supervisé pour lesquels obtenir des données est une tâche aisée mais les étiqueter est coûteux. En règle générale, c’est une stratégie de requête, une heuristique gloutonne basée sur un critère de sélection qui recherche les données non étiquetées potentiellement les plus intéressantes pour former ainsi un ensemble d'apprentissage. Une stratégie de requête est donc une procédure d'échantillonnage biaisée puisqu'elle favorise systématiquement certaines observations s'écartant ainsi des modèles d'échantillonnages indépendants et identiquement distribués. L'hypothèse principale de cette thèse s'inscrit dans la réduction du biais introduit par le critère de sélection. La proposition générale consiste à réduire le biais en sélectionnant le sous-ensemble minimal d'apprentissage pour lequel l'estimation de la loi de probabilité est aussi proche que possible de la loi sous-jacente prenant en compte l’intégralité des observations. Pour ce faire, une nouvelle stratégie générale de requête pour l'apprentissage actif a été mise au point utilisant la théorie de l'Information. Les performances de la stratégie de requête proposée ont été évaluées sur des données réelles et simulées. Les résultats obtenus confirment l'hypothèse sur le biais et montrent que l'approche envisagée améliore l'état de l'art sur différents jeux de données. / Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.

Ce travail a pour objet le développement d’un système combinant un algorithme génétique d’optimisation multiobjectifs avec des outils de thermodynamique prédictive de type calphad (calcul des diagrammes de phases) et de fouille de données permettant l’estimation des propriétés thermochimiques et thermomécaniques d’alliages multicomposants. L’intégration de ces techniques permet l’optimisation quasi-autonome de la composition d’alliages complexes vis-à-vis de plusieurs critères antagonistes telles les résistances mécaniques et chimiques, la stabilité microstructurelle à haute température et le coût. La méthode est complétée d’une technique d’analyse décisionnelle multicritères pour assister la sélection d’alliages. L’approche est illustrée par l’optimisation de la chimie de deux familles d’alliages multicomposants. Le premier cas d’étude porte sur les superalliages à base de nickel polycristallins corroyés renforcés par précipitation de la phase 0 destinés à la fabrication de disques de turbines dans l’aéronautique ou de tuyauteries de centrales thermiques. L’optimisation résulte en la conception d’alliages moins onéreux et prédits plus résistants que l’Inconel 740H et le Haynes 282, deux superalliages de dernière génération. Le second cas d’étude concerne les alliages dits « à forte entropie » dont la métallurgie singulière est emblématique des problèmes combinatoires. À l’issue de l’optimisation, quelques alliages à forte entropie ont été sélectionnés et fabriqués ; leur caractérisation expérimentale préliminaire met en évidence des propriétés attrayantes tel un ratio dureté sur masse volumique inédit. / The present work revolves around the development of an integrated system combining a multi-objective genetic algorithm with calphad-type computational thermodynamics (calculations of phase diagrams) and data mining techniques enabling the estimation of thermochemical and thermomechanical properties of multicomponent alloys. This integration allows the quasiautonomous chemistry optimisation of complex alloys against antagonistic criteria such as mechanical and chemical resistance, high-temperature microstructural stability, and cost. Further alloy selection capability is provided by a multi-criteria decision analysis technique. The proposed design methodology is illustrated on two multicomponent alloy families. The first case study relates to the design of wrought, polycrystalline 0-hardened nickel-base superalloys intended for aerospace turbine disks or tubing applications in the energy industry. The optimisation leads to the discovery of novel superalloys featuring lower costs and higher predicted strength than Inconel 740H and Haynes 282, two state-of-the-art superalloys. The second case study concerns the so-called “high-entropy alloys” whose singular metallurgy embodies typical combinatorial issues. Following the optimisation, several high-entropy alloys are produced; preliminary experimental characterisation highlights attractive properties such as an unprecedented hardness to density ratio.

The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them. In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name. In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase. Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities. The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set. The software that has been implemented and used in this project has been implemented in C.

Classification of terrain cover from satellite radar imagery represents an area of considerable current interest and research. Most satellite sensors used for land applications are of the imaging type. They record data in a variety of spectral channels and at a variety of ground resolutions. Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral information as the basis for automated land cover classification. A number of methods have been developed in the past to classify pixels [resolution cells] from multispectral imagery to a priori given land cover categories. Their ability to provide land cover information with high classification accuracies is significant for work where accurate and reliable thematic information is needed. The current trend towards the use of more spectral bands on satellite instruments, such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions will offer more precise possibilities for accurate identification. But as the complexity of the data grows, so too does the need for more powerful tools to analyse them. It is the major objective of this study to analyse the capabilities and applicability of the neural pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and Adaptive Resonance Theory (ART) by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of category choice, search and learning. The paper describes design features, system dynamics and simulation algorithms of this learning system, which is trained and tested for classification (8 a priori given classes) of a multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum likelihood classifier as conventional statistical benchmark on the same database. Both neural classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy ARTMAP leads to out-of-sample classification accuracies, very close to maximum performance, while the multi-layer perceptron - like the conventional classifier - shows difficulties to distinguish between some land use categories. (authors' abstract) / Series: Discussion Papers of the Institute for Economic Geography and GIScience