Zugl.: Hannover, Univ., Diss., 2007

Esse trabalho tem como objetivo estudar os mecanismos que propiciaram Francisco Schmidt a deixar sua condição de colono e imigrante, passando a participar ativamente da sociedade de forma política e econômica, na região de Ribeirão Preto do final do século XIX e início do século XX. Além disso, busca estudar as tomadas políticas em relação à cafeicultura paulista, suas intenções e seus reais efeitos na economia regional, a criação da especulação imobiliária da região, na época, a necessidade de melhores meios de transporte, seus incentivos e objetivos, o funcionalismo colonial dado no período sob as políticas regentes e, finalmente, o capital estrangeiro propiciado por empresas como Theodor Wille & Cia, e como esses fatores ajudaram Francisco Schmidt e suas atuações inovadoras a alcançarem sucesso e êxito, permitindo um imigrante alemão a se tornar um Rei do Café. / This work aims to study the mechanisms that allowed Francisco Schmidt to leave his status as colonist and immigrant, starting to participate actively in politics and economically in the region of Ribeirão Preto in the late nineteenth and early twentieth century. In addition, it seeks to study the political take of São Paulo\'s coffee industry, its intentions and its real effects on the regional economy, the creation of real estate speculation in the region, at the time, the need for better means of transport, its incentives and objectives, functionalism colonialism given in the period under the governing policies and finally the foreign capital provided by companies like Theodor Wille & Co., and how these factors helped Francisco Schmidt and his innovative actions to achieve success and success, allowing a German immigrant to become a \' King of the Café \'.

Cette thèse est consacrée à l’extension au cadre Banachique de la notion d’opérateur de Hilbert-Schmidt. Dans un premier temps, on étudie d’une part les opérateurs p-sommants dans un espace de Banach X vers un autre espace de Banach Y et d’autre part, les opérateurs gamma-radonifiants dans un espace de Hilbert vers un autre espace de Banach.Dans un second temps, on s'intéresse aux opérateurs gamma-sommants dans des espaces de Banach, qui coïncident avec les opérateurs de Rademacher-bornés, ce qui nous amène aux opérateurs presque sommants. Enfin, on en déduit plusieurs généralisations naturelles de la notion d’opérateur de Hilbert-Schmidt aux espaces de Banach.-Les classes des opérateurs p-sommants de X dans Y .-La classe des opérateurs presque sommants de X dans Y qui coïncide avec la classe des opérateurs gamma-radonifiants de X dans Y.-La classe des opérateurs faible* 1-nucléaires de X dans Y. / This thesis is devoted to extending the notion of Banach Hilbert-Schmidt operator to the framework of Banach spaces. In a first step, we study p-summing operators from a Banach space X into a Banach space Y and gamma-radoniyfing operators from a Hilbert space into a Banach space. In a second step, we discuss gamma-summing operators between Banach spaces, which coincide with Rademacher-bounded operators, which leads to the notion of almost summing operators. Finally, we present serval natural generalizations of the notion of Hilbert-Schmidt operator to Banach spaces.- Classes of p-summing operators from X into Y. - The class of almost summing operators from X into Y, which coincides with the class of gamma-radoniyfing operators from X into Y.- The class of weak*1-nuclear operators from X into Y.

Stellungsnahme des Musikwissenschaftlers Wilhelm Sinkovicz gegen die von Edeltraud Spaude veröffentlichte Rezension zu Carmen Ottner (Hg.): Apokalypse. Leipzig 2004

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I give an overview of the basic theory of Hilbert spaces necessary to understand the convergence of the Fourier series for square integrable functions. I state the necessary theorems and definitions to understand the formulations of the problem in a Hilbert space framework, and then I give some applications of the theory along the way.

DNA microarrays are capable of measuring expression levels of thousands of genes, even the whole genome, in a single experiment. Based on this, they have been widely used to extend the studies of cancerous tissues to a genomic level. One of the main goals in DNA microarray experiments is to identify a set of relevant genes such that the desired outputs of the experiment mostly depend on this set, to the exclusion of the rest of the genes. This is motivated by the fact that the biological process in cell typically involves only a subset of genes, and not the whole genome. The task of selecting a subset of relevant genes is called feature (gene) selection. Herein, we propose a feature selection algorithm for gene expression data. It is based on the Hilbert-Schmidt independence criterion, and partly motivated by Rank-One Downdate (R1D) and the Singular Value Decomposition (SVD). The algorithm is computationally very fast and scalable to large data sets, and can be applied to response variables of arbitrary type (categorical and continuous). Experimental results of the proposed technique are presented on some synthetic and well-known microarray data sets. Later, we discuss the capability of HSIC in providing a general framework which encapsulates many widely used techniques for dimensionality reduction, clustering and metric learning. We will use this framework to explain two metric learning algorithms, namely the Fisher discriminant analysis (FDA) and closed form metric learning (CFML). As a result of this framework, we are able to propose a new metric learning method. The proposed technique uses the concepts from normalized cut spectral clustering and is associated with an underlying convex optimization problem.

Texte remanié de: Habilitationsschrift--Freiburg--Universität, 1994. / Bibliogr. p. 356-390. Index.

DNA microarrays are capable of measuring expression levels of thousands of genes, even the whole genome, in a single experiment. Based on this, they have been widely used to extend the studies of cancerous tissues to a genomic level. One of the main goals in DNA microarray experiments is to identify a set of relevant genes such that the desired outputs of the experiment mostly depend on this set, to the exclusion of the rest of the genes. This is motivated by the fact that the biological process in cell typically involves only a subset of genes, and not the whole genome. The task of selecting a subset of relevant genes is called feature (gene) selection. Herein, we propose a feature selection algorithm for gene expression data. It is based on the Hilbert-Schmidt independence criterion, and partly motivated by Rank-One Downdate (R1D) and the Singular Value Decomposition (SVD). The algorithm is computationally very fast and scalable to large data sets, and can be applied to response variables of arbitrary type (categorical and continuous). Experimental results of the proposed technique are presented on some synthetic and well-known microarray data sets. Later, we discuss the capability of HSIC in providing a general framework which encapsulates many widely used techniques for dimensionality reduction, clustering and metric learning. We will use this framework to explain two metric learning algorithms, namely the Fisher discriminant analysis (FDA) and closed form metric learning (CFML). As a result of this framework, we are able to propose a new metric learning method. The proposed technique uses the concepts from normalized cut spectral clustering and is associated with an underlying convex optimization problem.

A main problem in machine learning is to predict the response variables of a test set given the training data and its corresponding response variables. A predictive model can perform satisfactorily only if the training data is an appropriate representative of the test data. This intuition is re???ected in the assumption that the training data and the test data are drawn from the same underlying distribution. However, the assumption may not be correct in many applications for various reasons. For example, gathering training data from the test population might not be easily possible, due to its expense or rareness. Or, factors like time, place, weather, etc can cause the difference in the distributions. I propose a method based on kernel distribution embedding and Hilbert Schmidt Independence Criteria (HSIC) to address this problem. The proposed method explores a new representation of the data in a new feature space with two properties: (i) the distributions of the training and the test data sets are as close as possible in the new feature space, (ii) the important structural information of the data is preserved. The algorithm can reduce the dimensionality of the data while it preserves the aforementioned properties and therefore it can be seen as a dimensionality reduction method as well. Our method has a closed-form solution and the experimental results on various data sets show that it works well in practice.