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A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

ARROIO, A.; LIMA, E. F.; HONORIO, K. M.; SILVA, A. B. F. da
Fonte: SPRINGER/PLENUM PUBLISHERS Publicador: SPRINGER/PLENUM PUBLISHERS
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
96.17%
The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active...

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

Lozano, N. B. H.; Weber, K. C.; Honorio, Káthia Maria; Guido, Rafael Victório Carvalho; Andricopulo, Adriano Defini; Da Silva, A. B. F.
Fonte: Wiley-Blackwell; Hoboken Publicador: Wiley-Blackwell; Hoboken
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
55.8%
Thiosemicarbazones are cruzain inhibitors which have been identified as potential antitrypanosomal agents. In this work, several molecular properties were calculated at the density functional theory (DFT)/B3LYP/6-311G* level for a set of 44 thiosemicarbazones. Unsupervised and supervised pattern recognition techniques (hierarchical cluster analysis, principal component analysis, kth-nearest neighbors, and soft independent modeling by class analogy) were used to obtain structureactivity relationship models, which are able to classify unknown compounds according to their activities. The chemometric analyses performed here revealed that 12 descriptors can be considered responsible for the discrimination between high and low activity compounds. Classification models were validated with an external test set, showing that predictive classifications were achieved with the selected variable set. The results obtained here are in good agreement with previous findings from the literature, suggesting that our models can be useful on further investigations on the molecular determinants for the antichagasic activity. (C) 2012 Wiley Periodicals, Inc.; FAPESP; FAPESP; CNPq; CNPq; CAPES; CAPES

Use of a gold microelectrode for discrimination of gunshot residues

Salles, Maiara Oliveira; Bertotti, Mauro; Paixão, Thiago Regis Longo Cesar da
Fonte: ELSEVIER SCIENCE SA; LAUSANNE Publicador: ELSEVIER SCIENCE SA; LAUSANNE
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.8%
Gunshot residues (GSR) can be used in forensic evaluations to obtain information about the type of gun and ammunition used in a crime. In this work, we present our efforts to develop a promising new method to discriminate the type of gun [four different guns were used: two handguns (0.38 revolver and 0.380 pistol) and two long-barrelled guns (12-calibre pump-action shotgun and 0.38 repeating rifle)] and ammunition (five different types: normal, semi-jacketed, full-jacketed, green, and 3T) used by a suspect. The proposed approach is based on information obtained from cyclic voltammograms recorded in solutions containing GSR collected from the hands of the shooters, using a gold microelectrode; the information was further analysed by non-supervised pattern-recognition methods [(Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA)]. In all cases (gun and ammunition discrimination), good separation among different samples in the score plots and dendrograms was achieved. (C) 2012 Elsevier B.V. All rights reserved.; FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo) [2006/60078-0, 2009/07859-1]; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); CAPES; CAPES; CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico); Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)

Supervised classification of basaltic aggregate particles based on texture properties

Gouveia, Lilian Tais de; Arruda, Guilherme Ferraz de; Rodrigues, Francisco Aparecido; Senger, Luciano José; Costa, Luciano da Fontoura
Fonte: American Society of Civil Engineers - ASCE; Reston Publicador: American Society of Civil Engineers - ASCE; Reston
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.8%
The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.; CNPq (301303/06-1, 305940/2010-4); FAPESP (05/00587-5, 10/19440-2, 07/01128-0)

Graph construction based on labeled instances for semi-supervised learning

Berton, Lilian; Lopes, Alneu de Andrade
Fonte: International Association of Pattern Recognition - IAPR; Linköping University; Lund University; Uppsala University; Institute of Electrical and Electronics Engineers - IEEE; Stockholm Publicador: International Association of Pattern Recognition - IAPR; Linköping University; Lund University; Uppsala University; Institute of Electrical and Electronics Engineers - IEEE; Stockholm
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
55.87%
Semi-Supervised Learning (SSL) techniques have become very relevant since they require a small set of labeled data. In this context, graph-based algorithms have gained prominence in the area due to their capacity to exploiting, besides information about data points, the relationships among them. Moreover, data represented in graphs allow the use of collective inference (vertices can affect each other), propagation of labels (autocorrelation among neighbors) and use of neighborhood characteristics of a vertex. An important step in graph-based SSL methods is the conversion of tabular data into a weighted graph. The graph construction has a key role in the quality of the classification in graph-based methods. This paper explores a method for graph construction that uses available labeled data. We provide extensive experiments showing the proposed method has many advantages: good classification accuracy, quadratic time complexity, no sensitivity to the parameter k > 10, sparse graph formation with average degree around 2 and hub formation from the labeled points, which facilitates the propagation of labels.; Sao Paulo Research Foundation (FAPESP) (Grant 2011/21880-3 and 2011/22749-8)

Avaliação de métodos ótimos e subótimos de seleção de características de texturas em imagens; Evaluation of optimal and suboptimal feature selection methods applied to image textures

Roncatti, Marco Aurelio
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 10/07/2008 PT
Relevância na Pesquisa
55.93%
Características de texturas atuam como bons descritores de imagens e podem ser empregadas em diversos problemas, como classificação e segmentação. Porém, quando o número de características é muito elevado, o reconhecimento de padrões pode ser prejudicado. A seleção de características contribui para a solução desse problema, podendo ser empregada tanto para redução da dimensionalidade como também para descobrir quais as melhores características de texturas para o tipo de imagem analisada. O objetivo deste trabalho é avaliar métodos ótimos e subótimos de seleção de características em problemas que envolvem texturas de imagens. Os algoritmos de seleção avaliados foram o branch and bound, a busca exaustiva e o sequential oating forward selection (SFFS). As funções critério empregadas na seleção foram a distância de Jeffries-Matusita e a taxa de acerto do classificador de distância mínima (CDM). As características de texturas empregadas nos experimentos foram obtidas com estatísticas de primeira ordem, matrizes de co-ocorrência e filtros de Gabor. Os experimentos realizados foram a classificação de regiôes de uma foto aérea de plantação de eucalipto, a segmentação não-supervisionada de mosaicos de texturas de Brodatz e a segmentação supervisionada de imagens médicas (MRI do cérebro). O branch and bound é um algoritmo ótimo e mais efiiente do que a busca exaustiva na maioria dos casos. Porém...

Um estudo sobre reconhecimento de padrões: um aprendizado supervisionado com classificador bayesiano; A study on pattern recognition: supervised learning with a Bayesian classier

Cerqueira, Pedro Henrique Ramos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 17/01/2011 PT
Relevância na Pesquisa
66.05%
A facilidade que temos para reconhecer um rosto, compreender palavras faladas, ler manuscritos, identicar chaves do carro no bolso e decidir se uma maçã está madura pelo seu cheiro, desmentem os processos complexos que estão por trás desses atos de reconhecer estes padrões. Estes reconhecimentos têm sido cruciais para a nossa sobrevivência, e ao longo das últimas dezenas de milhões de anos desenvolvemos sistemas sosticados para a realização dessas tarefas. O reconhecimento de padrões tem por objetivo realizar a classicação de determinado conjunto de dados em determinadas classes ou grupos, considerando os seus padrões e os das classes, permitindo diversas aplicações, como por exemplo: processamento de documentos, leitores de código de barra; identicação de pessoas, leitores óticos ou de impressão digital; automação industrial, processamento de imagens e aplicações agronômicas, análise de marcadores moleculares e classicação de plantas, tornando-se nos últimos anos, uma técnica de grande importância. Para uma melhor classicação é necessário realizar aprendizados, que podem ser elaborados pelo método supervisionado ou não supervisionado, a m de desenvolver os classicadores, tais como o classicador bayesiano e as redes neurais...

Efficient supervised optimum-path forest classification for large datasets

Papa, Joao P.; Falcao, Alexandre X.; de Albuquerque, Victor Hugo C.; Tavares, Joao Manuel R. S.
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Artigo de Revista Científica Formato: 512-520
ENG
Relevância na Pesquisa
55.9%
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Processo FAPESP: 09/16206-1; Processo FAPESP: 07/52015-0; Today data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without losing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the training samples. In this forest, each class may be represented by multiple trees rooted at some representative samples. The forest is a classifier that assigns to a new sample the label of its most strongly connected root. The methodology has been successfully used with different graph topologies and learning techniques. In this work, we have focused on one of the supervised approaches, which has offered considerable advantages over Support Vector Machines and Artificial Neural Networks to handle large datasets. We propose (i) a new algorithm that speeds up classification and (ii) a solution to reduce the training set size with negligible effects on the accuracy of classification...

Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms

Sensinger, Jonathon W.; Lock, Blair A.; Kuiken, Todd A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.06%
Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller...

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
65.86%
As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.

The use of a combined portable X ray ???uorescence and multivariate statistical methods to assess a validated macroscopic rock samples classi???cation in an ore exploration survey

Figueroa-Cisterna, Juan; Bagur-Gonz??lez, Mar??a Gracia; Morales-Ruano, Salvador; Carrillo-Ros??a, Javier; Mart??n-Peinado, Francisco
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
55.74%
El art??culo original ha sido publicado por Talanta, de la Editorial Elsevier, disponible en: http://www.sciencedirect.com/science/article/pii/S0039914011006096 El enlace v??a DOI es: doi:10.1016/j.talanta.2011.07.034; The combination of ???ex situ??? portable X ray ???uorescence with unsupervised and supervised pattern recog-nition techniques such as hierarchical cluster analysis, principal components analysis, factor analysis andlinear discriminant analysis have been applied to rock samples, in order to validate a ???in situ??? macro-scopic rock samples classi???cation of samples collected in the Boris Angelo mining area (Central Chile),during a drill-hole survey carried out to evaluate the economic potential of this Cu deposit. The analysedelements were Ca, Cu, Fe, K, Mn, Pb, Rb, Sr, Ti and Zn. The statistical treatment of the geological datahas been arisen from the application of the Box???Cox transformation used to transform the data set innormal form to minimize the non-normal distribution of the data. From the statistical results obtained itcan be concluded that the macroscopic classi???cation applied to the transformed data permits at least, todistinguish quite well in relation to two of the rock classes de???ned (70.5% correctly classi???ed (p < 0.05))as well as for four of the ???ve alteration types de???ned ???in situ??? (75% of the total samples).

Application of Pattern Recognition Techniques to Monitoring-While-Drilling on a Rotary Electric Blasthole Drill at an Open-Pit Coal Mine

Martin Gonzalez, Jorge Eduardo Jose
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1566535 bytes; application/pdf
EN; EN
Relevância na Pesquisa
46.09%
This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models.; Thesis (Master...

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lu, Hongfei; Jiang, Wu; Ghiassi, M.; Lee, Sean; Nitin, Mantri
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 03/01/2012 EN
Relevância na Pesquisa
56.05%
Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques...

Out-of-sample generalizations for supervised manifold learning for classification

Vural, Elif; Guillemot, Christine
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/02/2015
Relevância na Pesquisa
55.92%
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding. Most nonlinear supervised manifold learning methods compute the embedding of the manifolds only at the initially available training points, while the generalization of the embedding to novel points, known as the out-of-sample extension problem in manifold learning, becomes especially important in classification applications. In this work, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function (RBF) interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with a progressive procedure. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

Feature Learning based Deep Supervised Hashing with Pairwise Labels

Li, Wu-Jun; Wang, Sheng; Kang, Wang-Cheng
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/11/2015
Relevância na Pesquisa
45.95%
Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on hand-crafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand-crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning. In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing~(DPSH), to perform simultaneous feature learning and hash-code learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.

Fast Supervised Hashing with Decision Trees for High-Dimensional Data

Lin, Guosheng; Shen, Chunhua; Shi, Qinfeng; Hengel, Anton van den; Suter, David
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
55.94%
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated the advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval performance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash functions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for high-dimensional data, our method is orders of magnitude faster than many methods in terms of training time.; Comment: Appearing in Proc. IEEE Conf. Computer Vision and Pattern Recognition...

Supervised Dictionary Learning and Sparse Representation-A Review

Gangeh, Mehrdad J.; Farahat, Ahmed K.; Ghodsi, Ali; Kamel, Mohamed S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/02/2015
Relevância na Pesquisa
55.9%
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solving problems such as denoising, inpainting, and coding, it may not lead to optimal solution in classification tasks, where the ultimate goal is to make the learned dictionary and corresponding sparse representation as discriminative as possible. This motivated the emergence of a new category of techniques, which is appropriately called supervised dictionary learning and sparse representation (S-DLSR), leading to more optimal dictionary and sparse representation in classification tasks. Despite many research efforts for S-DLSR, the literature lacks a comprehensive view of these techniques, their connections, advantages and shortcomings. In this paper, we address this gap and provide a review of the recently proposed algorithms for S-DLSR. We first present a taxonomy of these algorithms into six categories based on the approach taken to include label information into the learning of the dictionary and/or sparse representation. For each category...

Supervised learning on graphs of spatio-temporal similarity in satellite image sequences

Héas, Patrick; Datcu, Mihai
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
45.97%
High resolution satellite image sequences are multidimensional signals composed of spatio-temporal patterns associated to numerous and various phenomena. Bayesian methods have been previously proposed in (Heas and Datcu, 2005) to code the information contained in satellite image sequences in a graph representation using Bayesian methods. Based on such a representation, this paper further presents a supervised learning methodology of semantics associated to spatio-temporal patterns occurring in satellite image sequences. It enables the recognition and the probabilistic retrieval of similar events. Indeed, graphs are attached to statistical models for spatio-temporal processes, which at their turn describe physical changes in the observed scene. Therefore, we adjust a parametric model evaluating similarity types between graph patterns in order to represent user-specific semantics attached to spatio-temporal phenomena. The learning step is performed by the incremental definition of similarity types via user-provided spatio-temporal pattern examples attached to positive or/and negative semantics. From these examples, probabilities are inferred using a Bayesian network and a Dirichlet model. This enables to links user interest to a specific similarity model between graph patterns. According to the current state of learning...

On the use of advanced pattern recognition techniques for the analysis of MRS and MRSI data in neuro-oncology

Ortega-Martorell, Sandra; Borrell, Joan
Fonte: [Barcelona] : Universitat Autònoma de Barcelona, Publicador: [Barcelona] : Universitat Autònoma de Barcelona,
Tipo: Tesis i dissertacions electròniques; info:eu-repo/semantics/doctoralThesis; info:eu-repo/semantics/publishedVersion Formato: application/pdf
Publicado em //2014 ENG
Relevância na Pesquisa
66.04%
El cáncer es una de las principales causas de muerte en el mundo. Los tumores cerebrales tienen una incidencia relativamente baja en comparación con otras patologías cancerígenas más generalizadas, pero la prognosis de algunos es muy pobre, contribuyendo significativamente a su morbilidad. La gestión clínica de una masa anormal en el cerebro es materia delicada y difícil, por lo que los expertos han de basarse en mediciones indirectas no invasivas de las características del tumor y de su crecimiento. En la práctica radiológica actual, estas mediciones se realizan a menudo mediante técnicas de resonancia magnética (MR), como la imagen (MRI) y la espectroscopia (MRS). La vasta información contenida en las señales de MR les hace ideales para la aplicación de técnicas de reconocimiento de patrones (PR). Durante las dos últimas décadas, estas técnicas se han aplicado con éxito al problema de la extracción de conocimiento a partir de datos de tumores cerebrales humanos, para su diagnóstico y pronóstico. No obstante, la discriminación de algunos tipos y subtipos de tumores, así como la delimitación precisa del área tumoral, continúan siendo un reto para los investigadores. En esta tesis, abordamos tales retos mediante la aplicación de un conjunto de técnicas avanzadas de PR. En primera instancia...

ADAPTIVE PATTERN RECOGNITION TO ENSURE CLINICAL VIABILITY OVER TIME

Sensinger, Jonathan W.; Lock, Blair A.; Kuiken, Todd A.
Fonte: Myoelectric Symposium Publicador: Myoelectric Symposium
Tipo: Artigo de Revista Científica
Publicado em //2008 EN_US
Relevância na Pesquisa
66.09%
Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. In order to be clinically viable over time, recognition paradigms must be capable of adapting with the user. Most existing paradigms are static, although two forms of adaptation have received limited attention: Supervised adaptation achieves high accuracy, since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without explicitly being told the intended class, thus achieving adaptation that is invisible to the user at the cost of reduced accuracy. This paper reports a novel adaptive experiment on eight subjects that allowed a post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 23%. Most unsupervised adaptation paradigms failed to achieve statistically significant reductions in error due to the uncertainty of the correct class. One method that selected high-confidence samples showed the most practical potential, although other methods warrant future investigation outside of a laboratory setting. The ability to provide supervised adaptation should be incorporated into any clinically viable pattern recognition controller...