This study seeks to investigate weather-related flight delay trends at 13 United States airports. Flight delay data were collected from 2004-2019 and normalized by airport operations data. Using Support Vector Regression (SVR), visual trends were identified. Further analysis was conducted by comparing all four meteorological seasons through computing 95% bootstrap confidence intervals on their means. Finally, precipitation and snowfall data were correlated with normalized delays to investigate how they are related. This study found that the season with the highest normalized delay values is heavily dependent upon location. Most airports saw a decrease in the SVR line at some point since 2004, but have since leveled off. It was also discovered that while precipitation trends are not changing drastically, delay variability has decreased at many airports in the last 10 years, which may be indicative of more effective mitigation strategies.

This dissertation considers certain issues in support vector machines (SVMs), including a description of their construction, aspects of certain exponential kernels used in some SVMs, and a presentation of an algorithm that computes the necessary elements of their operation with proof of convergence. In its first section, this dissertation provides a reasonably complete description of SVMs and their theoretical basis, along with a few motivating examples and counterexamples. This section may be used as an accessible, stand-alone introduction to the subject of SVMs for the advanced undergraduate. Its second section provides a proof of the positive-definiteness of a certain useful function here called E and dened as follows: Let V be a complex inner product space. Let N be a function that maps a vector from V to its norm. Let p be a real number between 0 and 2 inclusive and for any in V , let ( be N() raised to the p-th power. Finally, let a be a positive real number. Then E() is exp(()). Although the result is not new (other proofs are known but involve deep properties of stochastic processes) this proof is accessible to advanced undergraduates with a decent grasp of linear algebra. Its final section presents an algorithm by Dr. Kallman (preprint), based on earlier Russian work by B.F. Mitchell, V.F Demyanov, and V.N. Malozemov, and proves its convergence. The section also discusses briefly architectural features of the algorithm expected to result in practical speed increases.

This thesis proposes a novel experimental environment (non-linear stochastic gradient descent, NL-SGD), as well as a novel online learning algorithm (OL SVM), for solving a classic nonlinear Soft Margin L1 Support Vector Machine (SVM) problem using a Stochastic Gradient Descent (SGD) algorithm. The NL-SGD implementation has a unique method of random sampling and alpha calculations. The developed code produces a competitive accuracy and speed in comparison with the solutions of the Direct L2 SVM obtained by software for Minimal Norm SVM (MN-SVM) and Non-Negative Iterative Single Data Algorithm (NN-ISDA). The latter two algorithms have shown excellent performances on large datasets; which is why we chose to compare NL-SGD and OL SVM to them. All experiments have been done under strict double (nested) cross-validation, and the results are reported in terms of accuracy and CPU times. OL SVM has been implemented within MATLAB and is compared to the classic Sequential Minimal Optimization (SMO) algorithm implemented within MATLAB's solver, fitcsvm. The experiments with OL SVM have been done using k-fold cross-validation and the results reported in % error and % speedup of CPU Time.

Descritores de características locais de imagens utilizados na representação de objetos têm se tornado muito populares nos últimos anos. Tais descritores têm a capacidade de caracterizar o conteúdo da imagem em dados compactos e discriminativos. As informações extraídas dos descritores são representadas por meio de vetores de características e são utilizados em várias aplicações, tais como reconhecimento de faces, cenas complexas e texturas. Neste trabalho foi explorada a análise e modelagem de descritores locais para caracterização de imagens invariantes a escala, rotação, iluminação e mudanças de ponto de vista. Esta tese apresenta três novos descritores locais que contribuem com o avanço das pesquisas atuais na área de visão computacional, desenvolvendo novos modelos para a caracterização de imagens e reconhecimento de imagens. A primeira contribuição desta tese é referente ao desenvolvimento de um descritor de imagens baseado no mapeamento das diferenças de nível de cinza, chamado Center-Symmetric Local Mapped Pattern (CS-LMP). O descritor proposto mostrou-se robusto a mudanças de escala, rotação, iluminação e mudanças parciais de ponto de vista, e foi comparado aos descritores Center-Symmetric Local Binary Pattern (CS-LBP) e Scale-Invariant Feature Transform (SIFT). A segunda contribuição é uma modificação do descritor CS-LMP, e foi denominada Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). O descritor inclui o cálculo do pixel central na modelagem matemática, caracterizando melhor o conteúdo da mesma. O descritor proposto apresentou resultados superiores aos descritores CS-LMP, SIFT e LIOP na avaliação de reconhecimento de cenas complexas. A terceira contribuição é o desenvolvimento de um descritor de imagens chamado Mean-Local Mapped Pattern (M-LMP) que captura de modo mais fiel pequenas transições dos pixels na imagem, resultando em um número maior de \"matches\" corretos do que os descritores CS-LBP e SIFT. Além disso, foram realizados experimentos para classificação de objetos usando as base de imagens Caltech e Pascal VOC2006, apresentando melhores resultados comparando aos outros descritores em questão. Tal descritor foi proposto com a observação de que o descritor LBP pode gerar ruídos utilizando apenas a comparação dos vizinhos com o pixel central. O descritor M-LMP insere em sua modelagem matemática o cálculo da média dos pixels da vizinhança, com o objetivo de evitar ruídos e deixar as características mais robustas. Os descritores foram desenvolvidos de tal forma que seja possível uma redução de dimensionalidade de maneira simples e sem a necessidade de aplicação de técnicas como o PCA. Os resultados desse trabalho mostraram que os descritores propostos foram robustos na descrição das imagens, quantificando a similaridade entre as imagens por meio da abordagem Bag-of-Features (BoF), e com isso, apresentando resultados computacionais relevantes para a área de pesquisa. / Local feature descriptors used in objects representation have become very popular in recent years. Such descriptors have the ability to characterize the image content in compact and discriminative data. The information extracted from descriptors is represented by feature vectors and is used in various applications such as face recognition, complex scenes and textures. In this work we explored the analysis and modeling of local descriptors to characterize invariant scale images, rotation, changes in illumination and viewpoint. This thesis presents three new local descriptors that contribute to the current research advancement in computer vision area, developing new models for the characterization of images and image recognition. The first contribution is the development of a descriptor based on the mapping of gray-level-differences, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor showed to be invariant to scale change, rotation, illumination and partial changes of viewpoint and compared to the descriptors Center-Symmetric Local Binary Pattern (CS-LBP) and Scale-Invariant Feature Trans- form (SIFT). The second contribution is a modification of the CS-LMP descriptor, which we call Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). The descriptor includes the central pixel in mathematical modeling to better characterize the image content. The proposed descriptor presented superior results to CS-LMP , SIFT and LIOP descriptors in evaluating recognition of complex scenes. The third proposal includes the development of an image descriptor called Mean-Local Mapped Pattern (M-LMP) capturing more accurately small transitions of pixels in the image, resulting in a greater number of \"matches\" correct than CS-LBP and SIFT descriptors. In addition, experiments for classifying objects have been achieved by using the images based Caltech and Pascal VOC2006, presenting better results compared to other descriptors in question. This descriptor was proposed with the observation that the LBP descriptor can gene- rate noise using only the comparison of the neighbors to the central pixel. The M-LMP descriptor inserts in their mathematical modeling the averaging of the pixels of the neighborhood, in order to avoid noise and leave the more robust features. The results of this thesis showed that the proposed descriptors were robust in the description of the images, quantifying the similarity between images using the Bag-of-Features approach (BoF), and thus, presenting relevant computational results for the research area.

Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

Descritores de características locais de imagens utilizados na representação de objetos têm se tornado muito populares nos últimos anos. Tais descritores têm a capacidade de caracterizar o conteúdo da imagem em dados compactos e discriminativos. As informações extraídas dos descritores são representadas por meio de vetores de características e são utilizados em várias aplicações, tais como reconhecimento de faces, cenas complexas e texturas. Neste trabalho foi explorada a análise e modelagem de descritores locais para caracterização de imagens invariantes a escala, rotação, iluminação e mudanças de ponto de vista. Esta tese apresenta três novos descritores locais que contribuem com o avanço das pesquisas atuais na área de visão computacional, desenvolvendo novos modelos para a caracterização de imagens e reconhecimento de imagens. A primeira contribuição desta tese é referente ao desenvolvimento de um descritor de imagens baseado no mapeamento das diferenças de nível de cinza, chamado Center-Symmetric Local Mapped Pattern (CS-LMP). O descritor proposto mostrou-se robusto a mudanças de escala, rotação, iluminação e mudanças parciais de ponto de vista, e foi comparado aos descritores Center-Symmetric Local Binary Pattern (CS-LBP) e Scale-Invariant Feature Transform (SIFT). A segunda contribuição é uma modificação do descritor CS-LMP, e foi denominada Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). O descritor inclui o cálculo do pixel central na modelagem matemática, caracterizando melhor o conteúdo da mesma. O descritor proposto apresentou resultados superiores aos descritores CS-LMP, SIFT e LIOP na avaliação de reconhecimento de cenas complexas. A terceira contribuição é o desenvolvimento de um descritor de imagens chamado Mean-Local Mapped Pattern (M-LMP) que captura de modo mais fiel pequenas transições dos pixels na imagem, resultando em um número maior de \"matches\" corretos do que os descritores CS-LBP e SIFT. Além disso, foram realizados experimentos para classificação de objetos usando as base de imagens Caltech e Pascal VOC2006, apresentando melhores resultados comparando aos outros descritores em questão. Tal descritor foi proposto com a observação de que o descritor LBP pode gerar ruídos utilizando apenas a comparação dos vizinhos com o pixel central. O descritor M-LMP insere em sua modelagem matemática o cálculo da média dos pixels da vizinhança, com o objetivo de evitar ruídos e deixar as características mais robustas. Os descritores foram desenvolvidos de tal forma que seja possível uma redução de dimensionalidade de maneira simples e sem a necessidade de aplicação de técnicas como o PCA. Os resultados desse trabalho mostraram que os descritores propostos foram robustos na descrição das imagens, quantificando a similaridade entre as imagens por meio da abordagem Bag-of-Features (BoF), e com isso, apresentando resultados computacionais relevantes para a área de pesquisa. / Local feature descriptors used in objects representation have become very popular in recent years. Such descriptors have the ability to characterize the image content in compact and discriminative data. The information extracted from descriptors is represented by feature vectors and is used in various applications such as face recognition, complex scenes and textures. In this work we explored the analysis and modeling of local descriptors to characterize invariant scale images, rotation, changes in illumination and viewpoint. This thesis presents three new local descriptors that contribute to the current research advancement in computer vision area, developing new models for the characterization of images and image recognition. The first contribution is the development of a descriptor based on the mapping of gray-level-differences, called Center-Symmetric Local Mapped Pattern (CS-LMP). The proposed descriptor showed to be invariant to scale change, rotation, illumination and partial changes of viewpoint and compared to the descriptors Center-Symmetric Local Binary Pattern (CS-LBP) and Scale-Invariant Feature Trans- form (SIFT). The second contribution is a modification of the CS-LMP descriptor, which we call Modified Center-Symmetric Local Mapped Pattern (MCS-LMP). The descriptor includes the central pixel in mathematical modeling to better characterize the image content. The proposed descriptor presented superior results to CS-LMP , SIFT and LIOP descriptors in evaluating recognition of complex scenes. The third proposal includes the development of an image descriptor called Mean-Local Mapped Pattern (M-LMP) capturing more accurately small transitions of pixels in the image, resulting in a greater number of \"matches\" correct than CS-LBP and SIFT descriptors. In addition, experiments for classifying objects have been achieved by using the images based Caltech and Pascal VOC2006, presenting better results compared to other descriptors in question. This descriptor was proposed with the observation that the LBP descriptor can gene- rate noise using only the comparison of the neighbors to the central pixel. The M-LMP descriptor inserts in their mathematical modeling the averaging of the pixels of the neighborhood, in order to avoid noise and leave the more robust features. The results of this thesis showed that the proposed descriptors were robust in the description of the images, quantifying the similarity between images using the Bag-of-Features approach (BoF), and thus, presenting relevant computational results for the research area.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Alternative splicing is a mechanism for generating different gene transcripts (called iso- forms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods in general, and EST analy- sis and machine learning algorithms in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this thesis, I first iden- tify alternative splicing exons by analyzing EST-genome alignment. Next, I explore the predictive power of a rich set of features that have been experimentally shown to affect al- ternative splicing. I use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons. My results show that simple, linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Finally, I use feature selection methods to identify computationally the most informative features for the prediction problem considered.

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The study explores binary classification with Support Vector Machines as means to predict a satisfaction score based on customer surveys in the customer supportdomain. Standard feature selection methods and their impact on results are evaluated and a feature scoring metric Log Odds Ratio is implemented for addressingasymmetrical class distributions. Results show that the feature selection andscoring methods implemented improve performance significantly. Results alsoshow that it is possible to get decent predictive values on test data based onlimited amount of training observations. However mixed results are presentedin a real-world application example as a there is a significant error rate fordiscriminating the minority class. We also show the negative effects of usingcommon metrics such as accuracy and f-measure for optimizing models whendealing with high-skew data in a classification context.

Computer-based drug design is a vital area of pharmaceutical chemistry; Quantitative Structure-Activity Relationships (QSARs), determined computationally from experimental observations, are crucial in identifying candidate drugs by early screening, saving time on synthesis and in vivo testing. This thesis investigates the viability and the practicalities of using Mass Spectra-based pseudo-molecular descriptors, in comparison with other molecular descriptor systems, to predict the carcinogenicity, mutagenicity and the Cltransport inhibiting ability of a variety of molecules, and in the first case, of chemotherapeutic drugs particularly. It does so by identifying a number of QSARs which link the physical properties of chemicals with their concomitant activities in a reliable and mathematical manner. First, this thesis confirms that carcinogenicity and mutagenicity are indeed predictable using a variety of Artificial Intelligence techniques, both supervised and unsupervised, information germane to pharmaceutical research groups interested in the preliminary screening of candidate anti-cancer drugs. Secondly, it demonstrates that Mass Spectral intensities possess great descriptive fidelity and shows that reducing the burden of dimensionality is not only important, but imperative; selecting this smaller set of orthogonal descriptors is best achieved using Principal Component Analysis as opposed to the selection of a set of the most frequent fragments, or the use of every peak up to a number determined by the boundaries of supervised learning. Thirdly, it introduces a novel system of backpropagation and demonstrates that it is more efficient than its principal competitor at monitoring a series of connection weights when applied to this area of research, which requires complex relationships. Finally, it promulgates some preliminary conclusions about which AI techniques are applicable to certain problem-scenarios, how these techniques might be applied, and the likelihood that that application will result in the identification of series of reliable QSARs.