Beurling-Landau densities of weighted Fekete sets and correlation kernel estimates

Let $Q$ be a suitable real function on $C$. An $n$-Fekete set corresponding to $Q$ is a subset ${Z_{n1}},\dotsb, Z_{nn}}$ of $C$ which maximizes the expression $\Pi^n_i_{

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Validación de los criterios de Johnson y Johnson (1992) como indicadores de actitudes cooperativas

El objetivo del trabajo es: Validar los criterios de Johnson y Johnson (1992) como indicadores de evaluación de actitudes cooperativas. Se plantea definir, contrastar y evaluar estos criterios, analizando el desarrollo de actitudes cooperativas en niños y niñas de 6 años, cuando realizan actividades en el aula. La contrastación de los datos obtenidos a partir de diferentes instrumentos y en diversos contextos, ha permitido valorar y reelaborar la propuesta inicial, para ofrecer unas orientaciones y criterios, más ajustados a la realidad estudiada, que puedan ser útiles a otros centros de características similares

Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches

We propose a new kernel estimation of the cumulative distribution function based on transformation and on bias reducing techniques. We derive the optimal bandwidth that minimises the asymptotic integrated mean squared error. The simulation results show that our proposed kernel estimation improves alternative approaches when the variable has an extreme value distribution with heavy tail and the sample size is small.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Comparação do ajuste dos modelos de morgan-mercer-flodin e de johnson-mehl-avrami na reação de precipitação na liga cu-3%al-5%ag

Em geral, os modelos de crescimento empregados na descrição de processos cinéticos do estado sólido utilizam as funções de Morgan-Mercer-Flodin (MMF) e Johnson-Mehl-Avrami (JMA). Neste trabalho comparou-se o comportamento das estimativas dos parâmetros dessas funções, em duas parametrizações, quando ajustadas aos dados experimentais de variação isotérmica da microdureza da liga Cu-3%Al-5%Ag com o tempo de envelhecimento, em cinco temperaturas diferentes. Na estimativa dos parâmetros aplicou-se o método dos mínimos quadrados à função linearizada do modelo e para o refinamento da solução, os procedimentos da regressão não linear. Foram obtidas as distribuições de freqüências das estimativas dos parâmetros, por simulação, e determinados seus erros relativos. Observou-se que, em uma das parametrizações das funções, a estimativa do parâmetro cinético apresentou maior estabilidade. Apesar dessas funções serem anteriormente consideradas distintas, verificou-se que a função de MMF é uma aproximação da função de JMA, sendo que esta, com a parametrização adequada, é a mais indicada para descrever o processo cinético considerado.

Resistência natural da madeira de Corymbia maculata (Hook.) K.D.Hill & L.A.S. Johnson a fungos e cupins xilófagos, em condições de laboratório

A pesquisa teve o objetivo de avaliar a resistência natural da madeira de Corymbia maculata a fungos e a cupins xilófagos, em condições de laboratório. De peças radiais (tábuas) que continham o cerne e o alburno intactos foram retirados corpos-de-prova de 2,00 x 2,00 x 1,00 cm, com a menor dimensão na direção tangencial (ensaio com fungos), e de 2,54 x 2,00 x 0,64 cm, com a maior dimensão na direção das fibras (ensaio com cupins), em quatro posições na direção medula-casca. As amostras foram submetidas à ação dos fungos Postia placenta, Neolentinus lepideus e Polyporus fumosus por 12 semanas, ou à ação de cupins do gênero Nasutitermes por 30 dias. Constatou-se que a resistência da madeira ao apodrecimento foi dependente da posição na direção medula-casca e dos fungos utilizados. As amostras retiradas nas posições mais externas do tronco foram mais deterioradas que as internas. Dentro de cada posição, os fungos causaram deterioração semelhante à madeira, exceto para a posição mais externa (alburno), em que o fungo P. fumosus causou menos deterioração que os demais. De modo geral, a madeira de C. maculata foi altamente resistente (posições internas) ou resistente (posições externas) aos fungos ensaiados. Somente para o fungo N. lepideus a posição mais externa foi moderadamente resistente. Quanto aos cupins, a resistência da madeira não foi afetada pela posição na direção medula-casca e apresentou uma baixa perda de massa para as posições analisadas. Além disto, os cupins causaram somente desgaste superficial à madeira, e morreram durante o ensaio, o que permitiu classificar a madeira de C.maculata como resistente aos cupins ensaiados.

Prognose da produção de Eucalyptus camaldulensis Dehnh. pela aplicação da função de distribuição S B de Johnson

O objetivo deste estudo foi realizar a prognose da produção de um povoamento de Eucalyptus camaldulensis Delnh, localizado em Cuiabá, MT. Na distribuição diamétrica, utilizou-se a Função S B de Johnson ajustada pelo método dos momentos. O modelo testado para expressar os atributos da floresta foi avaliado por meio de análise de regressão. De maneira geral, com os testes realizados foi possível verificar que o modelo apresentou ajuste satisfatório e sem tendência nos resíduos. A eficiência de prognose foi avaliada pelo teste "t", desvio de prognose e correlação entre o volume prognosticado e o volume observado na idade de prognose. O processo de modelagem utilizado permitiu obter, com detalhes, as análises das tendências do crescimento, a partir das quais se pode concluir que a metodologia adotada permitiu a obtenção de estimativas da produção atual e futura, utilizando-se de um conjunto de modelos biomatemáticos discriminados em cada fase deste estudo.

Proposta metodológica para o ajuste ótimo da distribuição diamétrica SB de Johnson

A distribuição de SB de Johnson tem ampla utilização na área florestal. Basicamente há cinco métodos para ajustar essa distribuição, e quatro deles consideram o parâmetro de locação (ε) e de escala (λ) como termos independentes que devem ser conhecidos para obter os demais parâmetros. Este trabalho foi desenvolvido visando propor uma nova metodologia para determinar os parâmetros de locação e de escala que otimizam o ajuste dos cinco métodos ao minimizar a estatística "dn" do teste de aderência de Kolmogorov-Smirnov. Posteriormente, com o objetivo de testar a metodologia proposta, utilizou-se o aplicativo de otimização não linear "Solver.xla" do Microsoft Excel 2000, definindo a função objetivo e restrições de cada método de ajuste. Como conclusão, percebeu-se que a metodologia proposta demonstrou constituir alternativa interessante de ajuste da distribuição SB de Johnson, possibilitando seu ajuste otimizado. Dessa forma, recomenda-se que a metodologia proposta seja amplamente empregada para fins de determinação dos parâmetros do modelo quando do ajuste dessa distribuição probabilística muito usada na área florestal.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

VALORES LIMÍTROFES PARA TRANSPIRAÇÃO, DESENVOLVIMENTO E CRESCIMENTO DE Corymbia citriodora (Hook.) K.D. Hill & L.A.S. Johnson EM RESPOSTA À DEFICIÊNCIA HÍDRICA NO SOLO1

RESUMO Quando a disponibilidade de água no solo é reduzida, as plantas respondem diminuindo a taxa de transpiração, o crescimento e o desenvolvimento, na tentativa de aclimatação à deficiência hídrica. Com o objetivo de quantificar essas respostas em mudas de Corymbia citriodora (Hook.) K.D. Hill & L.A.S. Johnson, foi utilizada a metodologia da fração de água transpirável no solo (FATS), em um experimento conduzido em casa de vegetação, sob o delineamento inteiramente casualizado, sendo dois níveis de suplementação hídrica, duas épocas de aplicação da deficiência hídrica e nove repetições. A FATS e os parâmetros de transpiração, crescimento e desenvolvimento foram medidos diariamente durante a aplicação da deficiência hídrica. A FATS crítica, em que a transpiração, o crescimento e desenvolvimento começam a ser reduzidos, foi distinta nas épocas de aplicação da deficiência hídrica devido à diferença das condições meteorológicas. Os valores elevados de FATS indicaram que a espécie possuiu boa aclimatação à deficiência hídrica no solo, quando comparada com as espécies anuais e outras perenes.

Functional characterization of acetylated Brazil nut (Bertholletia excelsa HBK) kernel globulin

Defatted Brazil nut kernel flour, a rich source of high quality proteins, is presently being utilized in the formulation of animal feeds. One of the possible ways to improve its utilization for human consumption is through improvement in its functional properties. In the present study, changes in some of the functional properties of Brazil nut kernel globulin were evaluated after acetylation at 58.6, 66.2 and 75.3% levels. The solubility of acetylated globulin was improved above pH 6.0 but was reduced in the pH range of 3.0-4.0. Water and oil absorption capacity, as well as the viscosity increased with increase in the level of acetylation. Level of modification also influenced the emulsifying capacity: decreased at pH 3.0, but increased at pH 7.0 and 9.0. Highest emulsion activity (approximately 62.2%) was observed at pH 3.0 followed by pH 9.0 and pH 7.0 and least (about 11.8%) at pH 5.0. Emulsion stability also followed similar behavior as that of emulsion activity.

Effects of the mechanical damage on the water absorption process by corn kernel

The purpose of this study was to investigate and model the water absorption process by corn kernels with different levels of mechanical damage Corn kernels of AG 1510 variety with moisture content of 14.2 (% d.b.) were used. Different mechanical damage levels were indirectly evaluated by electrical conductivity measurements. The absorption process was based on the industrial corn wet milling process, in which the product was soaked with a 0.2% sulfur dioxide (SO2) solution and 0.55% lactic acid (C3H6O3) in distilled water, under controlled temperatures of 40, 50, 60, and 70 ºC and different mechanical damage levels. The Peleg model was used for the analysis and modeling of water absorption process. The conclusion is that the structural changes caused by the mechanical damage to the corn kernels influenced the initial rates of water absorption, which were higher for the most damaged kernels, and they also changed the equilibrium moisture contents of the kernels. The Peleg model was well adjusted to the experimental data presenting satisfactory values for the analyzed statistic parameters for all temperatures regardless of the damage level of the corn kernels.

Characterization, physicochemical stability, and evaluation of in vitro digestibility of solid lipid microparticles produced with palm kernel oil and tristearin

Solid lipid particles have been investigated by food researchers due to their ability to enhance the incorporation and bioavailability of lipophilic bioactives in aqueous formulations. The objectives of this study were to evaluate the physicochemical stability and digestibility of lipid microparticles produced with tristearin and palm kernel oil. The motivation for conducting this study was the fact that mixing lipids can prevent the expulsion of the bioactive from the lipid core and enhance the digestibility of lipid structures. The lipid microparticles containing different palm kernel oil contents were stable after 60 days of storage according to the particle size and zeta potential data. Their calorimetric behavior indicated that they were composed of a very heterogeneous lipid matrix. Lipid microparticles were stable under various conditions of ionic strength, sugar concentration, temperature, and pH. Digestibility assays indicated no differences in the release of free fatty acids, which was approximately 30% in all analises. The in vitro digestibility tests showed that the amount of palm kernel in the particles did not affect the percentage of lipolysis, probably due to the high amount of surfactants used and/or the solid state of the microparticles.