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Link de la búsquedaMathematical Methodologies in Pattern Recognition and Machine Learning / SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred (2013)
Título : Mathematical Methodologies in Pattern Recognition and Machine Learning : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009 num. 30 Número de páginas: VIII, 196 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-5076-4 Idioma : Inglés (eng) Palabras clave: Mathematics Computer science Pattern recognition System theory Mathematical optimization Systems Theory, Control Optimization Math Applications in Science Recognition Clasificación: 51 Matemáticas Resumen: This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists Nota de contenido: On order equivalences between distance and similarity measures on sequences and trees -- Scalable Corpus Annotation by Graph Construction and Label Propagation -- Computing the reeb graph for triangle meshes with active contours -- Efficient Computation of Voronoi Neighbors based on Polytope search in Pattern Recognition -- Estimation of the common oscillation for Phase Locked Matrix Factorization -- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines -- Pitch-sensitive Components emerge from Hierarchical Sparse Coding of Natural Sounds -- Generative Embeddings based on Rican Mixtures: Application to KernelBased Discriminative Classification of Magnetic Resonance Images.-Single-Frame Signal Recovery Using a Similarity-Prior Based on Pearson Type VII MRF -- Tracking solutions of time varying linear inverse problems -- Stacked Conditional Random Fields Exploiting Structural Consistencies -- Segmentation of Vessel Geometries from Medical Images using GPF Deformable Model -- Robust Deformable Model for Segmenting the Left Ventricle in 3D volumes of Ultrasound Data -- Algorithm to maintain linear element in 3D Level Set Topology Optimization -- Facial Expression recognition using Log-Euclidean statistical shape models En línea: http://dx.doi.org/10.1007/978-1-4614-5076-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32239 Mathematical Methodologies in Pattern Recognition and Machine Learning : Contributions from the International Conference on Pattern Recognition Applications and Methods, 2012 [documento electrónico] / SpringerLink (Online service) ; Pedro Latorre Carmona ; J. Salvador Sánchez ; Ana L. N. Fred . - New York, NY : Springer New York : Imprint: Springer, 2013 . - VIII, 196 p : online resource. - (Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009; 30) .
ISBN : 978-1-4614-5076-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer science Pattern recognition System theory Mathematical optimization Systems Theory, Control Optimization Math Applications in Science Recognition Clasificación: 51 Matemáticas Resumen: This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 2012. The conference provided a major point of collaboration between researchers, engineers and practitioners in the areas of Pattern Recognition, both from theoretical and applied perspectives, with a focus on mathematical methodologies. Contributions describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, and experimental and theoretical studies which yield new insights that provide key advances in the field. This book will be suitable for scientists and researchers in optimization, numerical methods, computer science, statistics and for differential geometers and mathematical physicists Nota de contenido: On order equivalences between distance and similarity measures on sequences and trees -- Scalable Corpus Annotation by Graph Construction and Label Propagation -- Computing the reeb graph for triangle meshes with active contours -- Efficient Computation of Voronoi Neighbors based on Polytope search in Pattern Recognition -- Estimation of the common oscillation for Phase Locked Matrix Factorization -- ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines -- Pitch-sensitive Components emerge from Hierarchical Sparse Coding of Natural Sounds -- Generative Embeddings based on Rican Mixtures: Application to KernelBased Discriminative Classification of Magnetic Resonance Images.-Single-Frame Signal Recovery Using a Similarity-Prior Based on Pearson Type VII MRF -- Tracking solutions of time varying linear inverse problems -- Stacked Conditional Random Fields Exploiting Structural Consistencies -- Segmentation of Vessel Geometries from Medical Images using GPF Deformable Model -- Robust Deformable Model for Segmenting the Left Ventricle in 3D volumes of Ultrasound Data -- Algorithm to maintain linear element in 3D Level Set Topology Optimization -- Facial Expression recognition using Log-Euclidean statistical shape models En línea: http://dx.doi.org/10.1007/978-1-4614-5076-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32239 Ejemplares
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Título : Technical Analysis for Algorithmic Pattern Recognition Tipo de documento: documento electrónico Autores: Prodromos E. Tsinaslanidis ; Achilleas D. Zapranis ; SpringerLink (Online service) Editorial: Cham : Springer International Publishing Fecha de publicación: 2016 Otro editor: Imprint: Springer Número de páginas: XIII, 204 p. 55 illus., 26 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-23636-0 Idioma : Inglés (eng) Palabras clave: Finance Pattern recognition Economics, Mathematical Statistics Econometrics Macroeconomics Finance, general for Business/Economics/Mathematical Finance/Insurance Recognition Quantitative Macroeconomics/Monetary Economics//Financial Economics Clasificación: 330 Economía en general Resumen: The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an "economic test" of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes. Nota de contenido: Technical Analysis -- Preprocessing Procedures -- Assessing the Predictive Performance of Technical Analysis -- Horizontal Patterns -- Zigzag Patterns -- Circular Patterns -- Technical Indicators -- A Statistical Assessment -- Dynamic Time Warping for Pattern Recognition En línea: http://dx.doi.org/10.1007/978-3-319-23636-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160 Technical Analysis for Algorithmic Pattern Recognition [documento electrónico] / Prodromos E. Tsinaslanidis ; Achilleas D. Zapranis ; SpringerLink (Online service) . - Cham : Springer International Publishing : Imprint: Springer, 2016 . - XIII, 204 p. 55 illus., 26 illus. in color : online resource.
ISBN : 978-3-319-23636-0
Idioma : Inglés (eng)
Palabras clave: Finance Pattern recognition Economics, Mathematical Statistics Econometrics Macroeconomics Finance, general for Business/Economics/Mathematical Finance/Insurance Recognition Quantitative Macroeconomics/Monetary Economics//Financial Economics Clasificación: 330 Economía en general Resumen: The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an "economic test" of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes. Nota de contenido: Technical Analysis -- Preprocessing Procedures -- Assessing the Predictive Performance of Technical Analysis -- Horizontal Patterns -- Zigzag Patterns -- Circular Patterns -- Technical Indicators -- A Statistical Assessment -- Dynamic Time Warping for Pattern Recognition En línea: http://dx.doi.org/10.1007/978-3-319-23636-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=42160 Ejemplares
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Título : Scientific Visualization: The Visual Extraction of Knowledge from Data Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Georges-Pierre Bonneau ; Thomas Ertl ; Gregory M. Nielson Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2006 Colección: Mathematics and Visualization, ISSN 1612-3786 Número de páginas: X, 434 p. 249 illus., 172 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-540-30790-7 Idioma : Inglés (eng) Palabras clave: Philosophy and science Computer graphics mathematics Mathematics Visualization of Science Imaging, Vision, Pattern Recognition Graphics Computational Engineering Clasificación: 51 Matemáticas Resumen: How to master, organize and extract useful knowledge from the overwhelming flow of information made available by today’s data acquisition systems and computing resources has become one of the greatest scientific challenges of the 21st century. Visualization is the premium means of taking up this challenge. This book is based on selected lectures given by leading experts in scientific visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The book contains more than 350 color illustrations Nota de contenido: Adaptive Contouring with Quadratic Tetrahedra -- On the Convexification of Unstructured Grids from a Scientific Visualization Perspective -- Brain Mapping Using Topology Graphs Obtained by Surface Segmentation -- Computing and Displaying Intermolecular Negative Volume for Docking -- Optimized Bounding Polyhedra for GPU-Based Distance Transform -- Generating, Representing and Querying Level-Of-Detail Tetrahedral Meshes -- Split’ N Fit: Adaptive Fitting of Scattered Point Cloud Data -- Ray Casting with Programmable Graphics Hardware -- Volume Exploration Made Easy Using Feature Maps -- Fantastic Voyage of the Virtual Colon -- Volume Denoising for Visualizing Refraction -- Emphasizing Isosurface Embeddings in Direct Volume Rendering -- Diagnostic Relevant Visualization of Vascular Structures -- Clifford Convolution and Pattern Matching on Irregular Grids -- Fast and Robust Extraction of Separation Line Features -- Fast Vortex Axis Calculation Using Vortex Features and Identification Algorithms -- Topological Features in Vector Fields -- Generalizing Focus+Context Visualization -- Rule-based Morphing Techniques for Interactive Clothing Catalogs -- A Practical System for Constrained Interactive Walkthroughs of Arbitrarily Complex Scenes -- Component Based Visualisation of DIET Applications -- Facilitating the Visual Analysis of Large-Scale Unsteady Computational Fluid Dynamics Simulations -- Evolving Dataflow Visualization Environments to Grid Computing -- Earthquake Visualization Using Large-scale Ground Motion and Structural Response Simulations En línea: http://dx.doi.org/10.1007/3-540-30790-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34929 Scientific Visualization: The Visual Extraction of Knowledge from Data [documento electrónico] / SpringerLink (Online service) ; Georges-Pierre Bonneau ; Thomas Ertl ; Gregory M. Nielson . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2006 . - X, 434 p. 249 illus., 172 illus. in color : online resource. - (Mathematics and Visualization, ISSN 1612-3786) .
ISBN : 978-3-540-30790-7
Idioma : Inglés (eng)
Palabras clave: Philosophy and science Computer graphics mathematics Mathematics Visualization of Science Imaging, Vision, Pattern Recognition Graphics Computational Engineering Clasificación: 51 Matemáticas Resumen: How to master, organize and extract useful knowledge from the overwhelming flow of information made available by today’s data acquisition systems and computing resources has become one of the greatest scientific challenges of the 21st century. Visualization is the premium means of taking up this challenge. This book is based on selected lectures given by leading experts in scientific visualization during a workshop held at Schloss Dagstuhl, Germany. Topics include user issues in visualization, large data visualization, unstructured mesh processing for visualization, volumetric visualization, flow visualization, medical visualization and visualization systems. The book contains more than 350 color illustrations Nota de contenido: Adaptive Contouring with Quadratic Tetrahedra -- On the Convexification of Unstructured Grids from a Scientific Visualization Perspective -- Brain Mapping Using Topology Graphs Obtained by Surface Segmentation -- Computing and Displaying Intermolecular Negative Volume for Docking -- Optimized Bounding Polyhedra for GPU-Based Distance Transform -- Generating, Representing and Querying Level-Of-Detail Tetrahedral Meshes -- Split’ N Fit: Adaptive Fitting of Scattered Point Cloud Data -- Ray Casting with Programmable Graphics Hardware -- Volume Exploration Made Easy Using Feature Maps -- Fantastic Voyage of the Virtual Colon -- Volume Denoising for Visualizing Refraction -- Emphasizing Isosurface Embeddings in Direct Volume Rendering -- Diagnostic Relevant Visualization of Vascular Structures -- Clifford Convolution and Pattern Matching on Irregular Grids -- Fast and Robust Extraction of Separation Line Features -- Fast Vortex Axis Calculation Using Vortex Features and Identification Algorithms -- Topological Features in Vector Fields -- Generalizing Focus+Context Visualization -- Rule-based Morphing Techniques for Interactive Clothing Catalogs -- A Practical System for Constrained Interactive Walkthroughs of Arbitrarily Complex Scenes -- Component Based Visualisation of DIET Applications -- Facilitating the Visual Analysis of Large-Scale Unsteady Computational Fluid Dynamics Simulations -- Evolving Dataflow Visualization Environments to Grid Computing -- Earthquake Visualization Using Large-scale Ground Motion and Structural Response Simulations En línea: http://dx.doi.org/10.1007/3-540-30790-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34929 Ejemplares
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Título : Computer Vision Using Local Binary Patterns Tipo de documento: documento electrónico Autores: Pietikäinen, Matti ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen Editorial: London : Springer London Fecha de publicación: 2011 Otro editor: Imprint: Springer Colección: Computational Imaging and Vision, ISSN 1381-6446 num. 40 Número de páginas: XVI, 212 p Il.: online resource ISBN/ISSN/DL: 978-0-85729-748-8 Idioma : Inglés (eng) Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Computer Vision Using Local Binary Patterns [documento electrónico] / Pietikäinen, Matti ; SpringerLink (Online service) ; Abdenour Hadid ; Guoying Zhao ; Timo Ahonen . - London : Springer London : Imprint: Springer, 2011 . - XVI, 212 p : online resource. - (Computational Imaging and Vision, ISSN 1381-6446; 40) .
ISBN : 978-0-85729-748-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer graphics Image processing Pattern recognition Biometrics (Biology) Mathematics, general Imaging, Vision, Recognition and Graphics Processing Vision Signal, Speech Clasificación: 51 Matemáticas Resumen: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: - Local binary patterns and their variants in spatial and spatiotemporal domains - Texture classification and segmentation, description of interest regions - Applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures - Background subtraction, recognition of actions - Face analysis using still images and image sequences, visual speech recognition - LBP in various applications Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision Nota de contenido: Background -- Local binary patterns for still images -- Spatiotemporal LBP -- Texture classification and segmentation -- Description of interest regions -- Applications in image retrieval and 3D recognition -- Recognition and segmentation of dynamic textures -- Background subtraction -- Recognition of actions -- Face analysis using still images -- Face analysis using image sequences -- Visual recognition of spoken phrases -- LBP in different applications En línea: http://dx.doi.org/10.1007/978-0-85729-748-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33135 Ejemplares
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Título : Methods of Optimization and Systems Analysis for Problems of Transcomputational Complexity Tipo de documento: documento electrónico Autores: Sergienko, Ivan V ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 72 Número de páginas: XIV, 226 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-4211-0 Idioma : Inglés (eng) Palabras clave: Mathematics Computer simulation graphics Mathematical optimization Optimization Simulation and Modeling Imaging, Vision, Pattern Recognition Graphics Clasificación: 51 Matemáticas Resumen: This work presents lines of investigation and scientific achievements of the Ukrainian school of optimization theory and adjacent disciplines. These include the development of approaches to mathematical theories, methodologies, methods, and application systems for the solution of applied problems in economy, finances, energy saving, agriculture, biology, genetics, environmental protection, hardware and software engineering, information protection, decision making, pattern recognition, self-adapting control of complicated objects, personnel training, etc. The methods developed include sequential analysis of variants, nondifferential optimization, stochastic optimization, discrete optimization, mathematical modeling, econometric modeling, solution of extremum problems on graphs, construction of discrete images and combinatorial recognition, etc. Some of these methods became well known in the world's mathematical community and are now known as classic methods Nota de contenido: Preface -- 1. Science Was the Meaning of His Life -- 2. Optimization Methods and Their Efficient Use -- 3. Mathematical Modeling and Analysis of Complex Processes on Supercomputer Systems -- 4. Problems of Modeling and Analysis of Processes in Economic Cybernetics -- 5. Problems of Solving Complicated Combinatorial Problems -- Afterward -- References -- Index En línea: http://dx.doi.org/10.1007/978-1-4614-4211-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32838 Methods of Optimization and Systems Analysis for Problems of Transcomputational Complexity [documento electrónico] / Sergienko, Ivan V ; SpringerLink (Online service) . - New York, NY : Springer New York : Imprint: Springer, 2012 . - XIV, 226 p : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 72) .
ISBN : 978-1-4614-4211-0
Idioma : Inglés (eng)
Palabras clave: Mathematics Computer simulation graphics Mathematical optimization Optimization Simulation and Modeling Imaging, Vision, Pattern Recognition Graphics Clasificación: 51 Matemáticas Resumen: This work presents lines of investigation and scientific achievements of the Ukrainian school of optimization theory and adjacent disciplines. These include the development of approaches to mathematical theories, methodologies, methods, and application systems for the solution of applied problems in economy, finances, energy saving, agriculture, biology, genetics, environmental protection, hardware and software engineering, information protection, decision making, pattern recognition, self-adapting control of complicated objects, personnel training, etc. The methods developed include sequential analysis of variants, nondifferential optimization, stochastic optimization, discrete optimization, mathematical modeling, econometric modeling, solution of extremum problems on graphs, construction of discrete images and combinatorial recognition, etc. Some of these methods became well known in the world's mathematical community and are now known as classic methods Nota de contenido: Preface -- 1. Science Was the Meaning of His Life -- 2. Optimization Methods and Their Efficient Use -- 3. Mathematical Modeling and Analysis of Complex Processes on Supercomputer Systems -- 4. Problems of Modeling and Analysis of Processes in Economic Cybernetics -- 5. Problems of Solving Complicated Combinatorial Problems -- Afterward -- References -- Index En línea: http://dx.doi.org/10.1007/978-1-4614-4211-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32838 Ejemplares
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