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The protein fold recognition problem is one of the important topics in biophysics. It is believed that the primary structure of a protein is helpful to drawing its three-dimensional (3D) structure. Given a target protein (a sequence of amino acids), the protein fold recognition problem is to decide which fold group of some protein structure database the target protein belongs to. Since more than two fold groups are to be located in this problem, it is a multi-class classification problem. Recently, many researchers have solved this problem by using the popular machine learning tools, such as neural networks (NN) and support vector machines (SVM). In this thesis, we use the SVM tool to solve this problem. Our strategy is to find out the effective features which can be used as an efficient guide to the classification problem. We build the feature preference table to help us to find out effective feature combinations quickly. We take 27 well-known fold groups in SCOP (Structural Classification of Proteins) as our data set. Our experimental results show that our method achieves the overall prediction accuracy of 61.4%, which is better than the previous method (56.5%). With the same feature combinations, our prediction accuracy is also higher than the previous results. These results show that our method is indeed effective for the fold recognition problem.

Wrinkling caused in wearing and laundry procedures is one of the most important performance properties of a fabric. Visual examination performed by trained experts is a routine wrinkle evaluation method in textile industry, however, this subjective evaluation is time-consuming. The need for objective, automatic and efficient methods of wrinkle evaluation has been increasing remarkably in recent years. In the present thesis, a wavelet transform based imaging analysis method was developed to measure the 2D fabric surface data captured by an infrared imaging system. After decomposing the fabric image by the Haar wavelet transform algorithm, five parameters were defined based on modified wavelet coefficients to describe wrinkling features, such as orientation, hardness, density and contrast. The wrinkle parameters provide useful information for textile, appliance, and detergent manufactures who study wrinkling behaviors of fabrics. A Support-Vector-Machine based classification scheme was developed for automatic wrinkle rating. Both linear kernel and radial-basis-function (RBF) kernel functions were used to achieve a higher rating accuracy. The effectiveness of this evaluation method was tested by 300 images of five selected fabric types with different fiber contents, weave structures, colors and laundering cycles. The results show agreement between the proposed wavelet-based automatic assessment and experts’ visual ratings. / text

Made available in DSpace on 2016-08-29T15:33:14Z (GMT). No. of bitstreams: 1 tese_4163_.pdf: 479699 bytes, checksum: 04f01a137084c0859b4494de6db8b3ac (MD5) Previous issue date: 2011-02-23 / Classificadores do tipo máquina de vetores de suporte (SVM) são atualmente considerados uma das técnicas mais poderosas para se resolver problemas de classificação com duas classes. Para aumentar o desempenho alcançado por classificadores SVM individuais, uma abordagem bem estabelecida é usar uma combinação de SVMs, a qual corresponde a um conjunto de classificadores SVMs que são, simultaneamente, individualmente precisos e coletivamente divergentes em suas decisões. Este trabalho propõe uma abordagem para se criar combinações de SVMs, baseada em um processo de três estágios. Inicialmente, são usadas execuções complementares de uma busca baseada em algoritmos genéticos (GEFS), com o objetivo de investigar globalmente o espaço de características para definir um conjunto de subconjuntos de características. Em seguida, para cada um desses subconjuntos de características definidos, uma SVM que usa parâmetros otimizados é construída. Por fim, é empregada uma busca local com o objetivo de selecionar um subconjunto otimizado dessas SVMs, e assim formar a combinação de SVMs que é finalmente produzida. Os experimentos foram realizados num contexto de detecção de defeitos em máquinas industriais. Foram usados 2000 exemplos de sinais de vibração de moto bombas instaladas em plataformas de petróleo. Os experimentos realizados mostram que o método proposto para se criar combinação de SVMs apresentou um desempenho superior em comparação a outras abordagens de classificação bem estabelecidas.

In this thesis, a support vector machine (SVM) is used to develop a model to predict Arias Intensity. Arias Intensity is a measure of the strength of ground motions that considers both the amplitude and the duration of ground motions. In this research, a subset of the database from the “Next Generation and the duration of Ground-Motion Attenuation Models” project was used as the training data. The data includes 3525 ground motion records from 175 earthquakes. This research provides the assessment of historical earthquakes using arias intensity data. Support vector machine uses a Kernel function to transform the data into a high dimensional space where relationships between the variables can be efficiently described using simpler models. In this research, after testing several kernel functions, a Gaussian Kernel was selected for the predictive model. The resulting model uses magnitude, epicentral distance, and the shear wave velocity as the predictor of Arias Intensity.

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.

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.

Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data sets involved. A classification algorithm is said to be scalable if there is no significant increase in time and space requirements for the algorithm (without compromising the generalization performance) when dealing with an increase in the training set size. Support Vector Machine (SVM) is one of the most celebrated kernel based classification methods used in Machine Learning. An SVM capable of handling large scale classification problems will definitely be an ideal candidate in many real world applications. The training process involved in SVM classifier is usually formulated as a Quadratic Programing(QP) problem. The existing solution strategies for this problem have an associated time and space complexity that is (at least) quadratic in the number of training points. This makes the SVM training very expensive even on classification problems having a few thousands of training examples. This thesis addresses the scalability of the training algorithms involved in both two class and multiclass Support Vector Machines. Efficient training schemes reducing the space and time requirements of the SVM training process are proposed as possible solutions. The classification schemes discussed in the thesis for handling large scale two class classification problems are a) Two selective sampling based training schemes for scaling Non-linear SVM and b) Clustering based approaches for handling unbalanced data sets with Core Vector Machine. To handle large scale multicalss classification problems, the thesis proposes Multiclass Core Vector Machine (MCVM), a scalable SVM based multiclass classifier. In MVCM, the multiclass SVM problem is shown to be equivalent to a Minimum Enclosing Ball (MEB) problem and is then solved using a fast approximate MEB finding algorithm. Experimental studies were done with several large real world data sets such as IJCNN1 and Acoustic data sets from LIBSVM page, Extended USPS data set from CVM page and network intrusion detection data sets of DARPA, US Defense used in KDD 99 contest. From the empirical results it is observed that the proposed classification schemes achieve good generalization performance at low time and space requirements. Further, the scalability experiments done with large training data sets have demonstrated that the proposed schemes scale well. A novel soft clustering scheme called Rough Support Vector Clustering (RSVC) employing the idea of Soft Minimum Enclosing Ball Problem (SMEB) is another contribution discussed in this thesis. Experiments done with a synthetic data set and the real world data set namely IRIS, have shown that RSVC finds meaningful soft cluster abstractions.

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.

In this thesis, fixed point theory is used to construct a fractal type sets and to solve data classification problem. Fixed point method, which is a beautiful mixture of analysis, topology, and geometry has been revealed as a very powerful and important tool in the study of nonlinear phenomena. The existence of fixed points is therefore of paramount importance in several areas of mathematics and other sciences. In particular, fixed points techniques have been applied in such diverse fields as biology, chemistry, economics, engineering, game theory and physics. In Chapter 2 of this thesis it is demonstrated how to define and construct a fractal type sets with the help of iterations of a finite family of generalized F-contraction mappings, a class of mappings more general than contraction mappings, defined in the context of b-metric space. This leads to a variety of results for iterated function system satisfying a different set of contractive conditions. The results unify, generalize and extend various results in the existing literature. In Chapter 3, the theory of support vector machine for linear and nonlinear classification of data and the notion of generalized support vector machine is considered. In the thesis it is also shown that the problem of generalized support vector machine can be considered in the framework of generalized variation inequalities and results on the existence of solutions are established. / FUSION