A área de detecção de outliers (ou detecção de anomalias) possui um papel fundamental na descoberta de padrões em dados que podem ser considerados excepcionais sob alguma perspectiva. Uma importante distinção se dá entre as técnicas supervisionadas e não supervisionadas. O presente trabalho enfoca as técnicas de detecção não supervisionadas. Existem dezenas de algoritmos desta categoria na literatura, porém cada um deles utiliza uma intuição própria do que deve ser considerado um outlier ou não, que é naturalmente um conceito subjetivo. Isso dificulta sensivelmente a escolha de um algoritmo em particular e também a escolha de uma configuração adequada para o algoritmo escolhido em uma dada aplicação prática. Isso também torna altamente complexo avaliar a qualidade da solução obtida por um algoritmo/configuração em particular adotados pelo analista, especialmente em função da problemática de se definir uma medida de qualidade que não seja vinculada ao próprio critério utilizado pelo algoritmo. Tais questões estão inter-relacionadas e se referem respectivamente aos problemas de seleção de modelos e avaliação (ou validação) de resultados em aprendizado de máquina não supervisionado. Neste trabalho foi desenvolvido um índice pioneiro para avaliação não supervisionada de detecção de outliers. O índice, chamado IREOS (Internal, Relative Evaluation of Outlier Solutions), avalia e compara diferentes soluções (top-n, i.e., rotulações binárias) candidatas baseando-se apenas nas informações dos dados e nas próprias soluções a serem avaliadas. O índice também é ajustado estatisticamente para aleatoriedade e extensivamente avaliado em vários experimentos envolvendo diferentes coleções de bases de dados sintéticas e reais. / Outlier detection (or anomaly detection) plays an important role in the pattern discovery from data that can be considered exceptional in some sense. An important distinction is that between the supervised and unsupervised techniques. In this work we focus on unsupervised outlier detection techniques. There are dozens of algorithms of this category in literature, however, each of these algorithms uses its own intuition to judge what should be considered an outlier or not, which naturally is a subjective concept. This substantially complicates the selection of a particular algorithm and also the choice of an appropriate configuration of parameters for a given algorithm in a practical application. This also makes it highly complex to evaluate the quality of the solution obtained by an algorithm or configuration adopted by the analyst, especially in light of the problem of defining a measure of quality that is not hooked on the criterion used by the algorithm itself. These issues are interrelated and refer respectively to the problems of model selection and evaluation (or validation) of results in unsupervised learning. Here we developed a pioneer index for unsupervised evaluation of outlier detection results. The index, called IREOS (Internal, Relative Evaluation of Outlier Solutions), can evaluate and compare different candidate (top-n, i.e., binary labelings) solutions based only upon the data information and the solution to be evaluated. The index is also statistically adjusted for chance and extensively evaluated in several experiments involving different collections of synthetic and real data sets.

Diss. (sammanfattning). Uppsala : Sveriges lantbruksuniv., 2003. / Härtill 5 uppsatser.

A área de detecção de outliers (ou detecção de anomalias) possui um papel fundamental na descoberta de padrões em dados que podem ser considerados excepcionais sob alguma perspectiva. Uma importante distinção se dá entre as técnicas supervisionadas e não supervisionadas. O presente trabalho enfoca as técnicas de detecção não supervisionadas. Existem dezenas de algoritmos desta categoria na literatura, porém cada um deles utiliza uma intuição própria do que deve ser considerado um outlier ou não, que é naturalmente um conceito subjetivo. Isso dificulta sensivelmente a escolha de um algoritmo em particular e também a escolha de uma configuração adequada para o algoritmo escolhido em uma dada aplicação prática. Isso também torna altamente complexo avaliar a qualidade da solução obtida por um algoritmo/configuração em particular adotados pelo analista, especialmente em função da problemática de se definir uma medida de qualidade que não seja vinculada ao próprio critério utilizado pelo algoritmo. Tais questões estão inter-relacionadas e se referem respectivamente aos problemas de seleção de modelos e avaliação (ou validação) de resultados em aprendizado de máquina não supervisionado. Neste trabalho foi desenvolvido um índice pioneiro para avaliação não supervisionada de detecção de outliers. O índice, chamado IREOS (Internal, Relative Evaluation of Outlier Solutions), avalia e compara diferentes soluções (top-n, i.e., rotulações binárias) candidatas baseando-se apenas nas informações dos dados e nas próprias soluções a serem avaliadas. O índice também é ajustado estatisticamente para aleatoriedade e extensivamente avaliado em vários experimentos envolvendo diferentes coleções de bases de dados sintéticas e reais. / Outlier detection (or anomaly detection) plays an important role in the pattern discovery from data that can be considered exceptional in some sense. An important distinction is that between the supervised and unsupervised techniques. In this work we focus on unsupervised outlier detection techniques. There are dozens of algorithms of this category in literature, however, each of these algorithms uses its own intuition to judge what should be considered an outlier or not, which naturally is a subjective concept. This substantially complicates the selection of a particular algorithm and also the choice of an appropriate configuration of parameters for a given algorithm in a practical application. This also makes it highly complex to evaluate the quality of the solution obtained by an algorithm or configuration adopted by the analyst, especially in light of the problem of defining a measure of quality that is not hooked on the criterion used by the algorithm itself. These issues are interrelated and refer respectively to the problems of model selection and evaluation (or validation) of results in unsupervised learning. Here we developed a pioneer index for unsupervised evaluation of outlier detection results. The index, called IREOS (Internal, Relative Evaluation of Outlier Solutions), can evaluate and compare different candidate (top-n, i.e., binary labelings) solutions based only upon the data information and the solution to be evaluated. The index is also statistically adjusted for chance and extensively evaluated in several experiments involving different collections of synthetic and real data sets.

As distribuições de probabilidade gama, normal inversa, Weibull, log-normal e exponencial são uma boa alternativa para modelar observações associadas ao tempo, pois, em geral, a variável tempo possui assimetria à esquerda ou à direita, o que caracteriza as distribuições citadas anteriormente. O objetivo deste trabalho constitui-se em avaliar o comportamento dos predadores, Euborellia annulipes (\"Tesourinha\") e Harmonia axyridis (\"Joaninha\"), em relação à praga conhecida como Aphis gossypii (\"Pulgão\"). Outra pretensão deste trabalho é a aplicação da modelagem estatística, dando ênfase as técnicas dos modelos lineares generalizados e análise de sobrevivência, as quais foram aplicadas aos dados provenientes de um experimento, instalado no Laboratório de Ecologia de Insetos da Escola Superior de Agricultura \"Luiz de Queiroz\" (ESALQ). O experimento foi composto por 21 repetições, sendo cada repetição efetuada por meio de uma placa de Petri medido 60 X 15 mm. Em cada placa foi liberado um pulgão adulto áptero na parte central, tendo três pesquisadores responsáveis por observar a varável definida como tempo de ataque. Inicialmente, foram ajustados os modelos com distribuição gama e diferentes funções de ligação, e o modelo com a distribuição normal inversa com função de ligação canônica. Esses modelos foram ajustados aos dados desconsiderando as censuras, em que por meio do gráfico half-normal plot e testes de hipóteses, verificou que o modelo com a distribuição normal inversa com função de ligação canônica, apresentou o melhor ajuste. Posteriormente, foram ajustados os modelos exponencial, Weibull e log-normal para os dados considerando as censuras, os quais foram avaliados mediante o teste de razão de verossimilhança, sendo o modelo log-normal mais apropriado aos dados. / The probability density function of gamma, inverse normal, Weibull, log-normal and exponential distributions are good alternatives for modelling observations related with time, since, in general, the time variable has left or right asymmetry, which characterizes the distributions previously mentioned . The aim of this work is the application of statistical modeling, emphasizing the techniques of generalized linear models and survival analysis, which were applied to data from an experiment, installed in the Laboratory of Insect Ecology of the \"Luiz de Queiroz\" College of Agriculture (ESALQ), in which the goal of this experiment was to evaluate the behavior of predators, Euborellia annulipes (\"ring-legged earwig\") and Harmonia axyridis (\"Ladybird\"), in relation to the pest known as Aphis. The experiment was composed of 21 replicates, each replicate being done by means of a petri dish measured 60 X 15 mm. On each plate an adult aphid was released in the central part, with three researchers responsible. The model with distribution was used to determine the variance, which was defined as the attack time. Normal distribution with canonical link function. These models were adjusted to the data disregarding censorship, in which through the half-normal plot and hypothesis tests, verified that the model with the normal inverse distribution with canonical link function, presented the best fit. Subsequently, the exponential, Weibull and log-normal models were adjusted for the data considering the censorship, which were evaluated by the likelihood ratio test, the log-normal model being more appropriate to the data.

As distribuições de probabilidade gama, normal inversa, Weibull, log-normal e exponencial são uma boa alternativa para modelar observações associadas ao tempo, pois, em geral, a variável tempo possui assimetria à esquerda ou à direita, o que caracteriza as distribuições citadas anteriormente. O objetivo deste trabalho constitui-se em avaliar o comportamento dos predadores, Euborellia annulipes (\"Tesourinha\") e Harmonia axyridis (\"Joaninha\"), em relação à praga conhecida como Aphis gossypii (\"Pulgão\"). Outra pretensão deste trabalho é a aplicação da modelagem estatística, dando ênfase as técnicas dos modelos lineares generalizados e análise de sobrevivência, as quais foram aplicadas aos dados provenientes de um experimento, instalado no Laboratório de Ecologia de Insetos da Escola Superior de Agricultura \"Luiz de Queiroz\" (ESALQ). O experimento foi composto por 21 repetições, sendo cada repetição efetuada por meio de uma placa de Petri medido 60 X 15 mm. Em cada placa foi liberado um pulgão adulto áptero na parte central, tendo três pesquisadores responsáveis por observar a varável definida como tempo de ataque. Inicialmente, foram ajustados os modelos com distribuição gama e diferentes funções de ligação, e o modelo com a distribuição normal inversa com função de ligação canônica. Esses modelos foram ajustados aos dados desconsiderando as censuras, em que por meio do gráfico half-normal plot e testes de hipóteses, verificou que o modelo com a distribuição normal inversa com função de ligação canônica, apresentou o melhor ajuste. Posteriormente, foram ajustados os modelos exponencial, Weibull e log-normal para os dados considerando as censuras, os quais foram avaliados mediante o teste de razão de verossimilhança, sendo o modelo log-normal mais apropriado aos dados. / The probability density function of gamma, inverse normal, Weibull, log-normal and exponential distributions are good alternatives for modelling observations related with time, since, in general, the time variable has left or right asymmetry, which characterizes the distributions previously mentioned . The aim of this work is the application of statistical modeling, emphasizing the techniques of generalized linear models and survival analysis, which were applied to data from an experiment, installed in the Laboratory of Insect Ecology of the \"Luiz de Queiroz\" College of Agriculture (ESALQ), in which the goal of this experiment was to evaluate the behavior of predators, Euborellia annulipes (\"ring-legged earwig\") and Harmonia axyridis (\"Ladybird\"), in relation to the pest known as Aphis. The experiment was composed of 21 replicates, each replicate being done by means of a petri dish measured 60 X 15 mm. On each plate an adult aphid was released in the central part, with three researchers responsible. The model with distribution was used to determine the variance, which was defined as the attack time. Normal distribution with canonical link function. These models were adjusted to the data disregarding censorship, in which through the half-normal plot and hypothesis tests, verified that the model with the normal inverse distribution with canonical link function, presented the best fit. Subsequently, the exponential, Weibull and log-normal models were adjusted for the data considering the censorship, which were evaluated by the likelihood ratio test, the log-normal model being more appropriate to the data.

The thesis "Methods to Assess IS/ICT Outsourcing Models" focuses mainly on an assessment and selection process of suitable IS/ICT outsourcing models. First, IS/ICT outsourcing concept is explained and its advantages as well as disadvantages are described. Second, IS/ICT outsourcing models (primarily classified by outsourcing subject) and their characteristics are described. Third, existing methods to assess and subsequently select suitable IS/ICT outsourcing models are analysed. Main goal of the thesis is to confront the existing methods with a process used in real-life IS/ICT outsourcing project and then to give recommendations for methods improvement. To achieve that goal the existing methods are compared with a process used in the real-life outsourcing project. Based on positively perceived parts of the existing methods as well as on flaws identified, an own method to assess and select IS/ICT outsourcing models is designed. The following methods are used when writing the thesis: logical/historical method, description, analysis and synthesis. Main contribution of the thesis to the topic is a design of the own method aiming at improvements to the existing methods so that it is as usable as possible in practice. This is achieved through a design of tools supporting crucial steps of the designed method. The fact that the author is member of a project team working on the real-life IS/ICT outsourcing project facilitates a contribution achievement too.

This study estimated soybean yield responses to K fertilizer using trials data from 18 sites in Mississippi from 2011-2016. Eight response functions were fitted, including linear, linear plateau, quadratic, quadratic-plateau, square root quadratic, spherical plateau, exponential, and exponential-plateau functions. The ratio of high responsive, low responsive, and no responsive sites to K rates is 3:3:12 respectively. The response functions led to different predicted optimal K rates, and the best response function for each site was determined by the Vuong closeness test and economic loss analysis. The predicted economically optimal K rates are 157, 73, and 0 lb/acre for high, low, and no response sites respectively, and the average optimal K rate is 55 lb/acre. Compared to the currently used regional uniform K fertilizer rate of 80 lb/acre, the response-based K rates are expected to generate soybean production gain of about $14 per acre for Mississippi soybean producers.

L'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parole / Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysis