Highdimensional Data
Mostrando 1-12 de 19 artigos, teses e dissertações.
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1. Maximal Information Coefficient and Support Vector Regression Based Nonlinear Feature Selection and QSAR Modeling on Toxicity of Alcohol Compounds to Tadpoles of Rana temporaria
Efficient evaluation of biotoxicity of organics is of vital significance to resource utilization and environmental protection. In this study, toxicity of 110 alcohol compounds to tadpoles of Rana temporaria is adopted as the dependent variable and 1388 physiochemical parameters (features) calculated by PCLIENT are used for representing each compound. A featu
J. Braz. Chem. Soc.. Publicado em: 2019-02
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2. Analysis of the immunological biomarker profile during acute Zika virus infection reveals the overexpression of CXCL10, a chemokine linked to neuronal damage
BACKGROUND Infection with Zika virus (ZIKV) manifests in a broad spectrum of disease ranging from mild illness to severe neurological complications and little is known about Zika immunopathogenesis. OBJECTIVES To define the immunologic biomarkers that correlate with acute ZIKV infection. METHODS We characterized the levels of circulating cytokines, chemok
Mem. Inst. Oswaldo Cruz. Publicado em: 14/05/2018
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3. How can I explore high-dimensional data?
Dental Press J. Orthod.. Publicado em: 2014-12
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4. Um estudo sobre o papel de medidas de similaridade em visualização de coleções de documentos / A study on the role of similarity measures in visual text analytics
Information visualization techniques, such as similarity based point placement, are used for generating of visual data representation that evidence some patterns. These techniques are sensitive to data quality, which depends of a very influential preprocessing step. This step involves cleaning the text and in some cases, detecting terms and their weights, as
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 27/09/2012
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5. Explorando conjuntos de dados volumétricos multidimensionais variantes no tempo usando projeções / Exploring time-varying multidimensional volumetric datasets using projections
The area of volume visualization encompasses a set of techniques used for representation, manipulation and display of data associated with a region of a volume, thus enabling the exploration and understanding of the interior of three-dimensional objects. However, some limitations are still encountered in this area. For example, the simultaneous exploration o
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 10/09/2012
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6. Soluções aproximadas para algoritmos escaláveis de mineração de dados em domínios de dados complexos usando GPGPU / On approximate solutions to scalable data mining algorithms for complex data problems using GPGPU
The increasing availability of data in diverse domains has created a necessity to develop techniques and methods to discover knowledge from huge volumes of complex data, motivating many research works in databases, data mining and information retrieval communities. Recent studies have suggested that searching in complex data is an interesting research field
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 22/09/2011
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7. On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data
It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the
Publicado em: 2011
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8. Support vector machines na classificação de imagens hiperespectrais / Hyperspectral image classification with support vector machines
É de conhecimento geral que, em alguns casos, as classes são espectralmente muito similares e que não é possível separá-las usando dados convencionais em baixa dimensionalidade. Entretanto, estas classes podem ser separáveis com um alto grau de acurácia em espaço de alta dimensão. Por outro lado, classificação de dados em alta dimensionalidade po
Publicado em: 2009
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9. DISTRIBUTIONS OF RETURNS, VOLATILITIES AND CORRELATIONS IN THE BRAZILIAN STOCK MARKET / DISTRIBUIÇÕES DE RETORNOS, VOLATILIDADES E CORRELAÇÕES NO MERCADO ACIONÁRIO BRASILEIRO
The normality assumption is commonly used in the risk management area to describe the distributions of returns standardized by volatilities. However, using five of the most actively traded stocks in Bovespa, this paper shows that this assumption is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when we use the informat
Publicado em: 2004
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10. MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets
Motivation: The study of complex biological relationships is aided by large and high-dimensional data sets whose analysis often involves dimension reduction to highlight representative or informative directions of variation. In principle, information theory provides a general framework for quantifying complex statistical relationships for dimension reduction
Oxford University Press.
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11. An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers
Discovery of “signature” protein profiles that distinguish disease states (eg, malignant, benign, and normal) is a key step towards translating recent advancements in proteomic technologies into clinical utilities. Protein data generated from mass spectrometers are, however, large in size and have complex features due to complexities in both biological
Hindawi Publishing Corporation.
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12. An insight into high-resolution mass-spectrometry data
Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical
Oxford University Press.