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Link de la búsquedaData Analysis and Decision Support / SpringerLink (Online service) ; Daniel Baier ; Reinhold Decker ; Schmidt-Thieme, Lars (2005)
Título : Data Analysis and Decision Support Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Daniel Baier ; Reinhold Decker ; Schmidt-Thieme, Lars Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2005 Colección: Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814 Número de páginas: XI, 352 p Il.: online resource ISBN/ISSN/DL: 978-3-540-28397-3 Idioma : Inglés (eng) Palabras clave: Statistics Information technology Business Data processing structures (Computer science) Mathematical statistics Computers Statistical Theory and Methods IT in Probability Computer Science Systems Communication Service Structures Clasificación: 51 Matemáticas Resumen: It is a great privilege and pleasure to write a foreword for a book honor ing Wolfgang Gaul on the occasion of his sixtieth birthday. Wolfgang Gaul is currently Professor of Business Administration and Management Science and the Head of the Institute of Decision Theory and Management Science, Faculty of Economics, University of Karlsruhe (TH), Germany. He is, by any measure, one of the most distinguished and eminent scholars in the world today. Wolfgang Gaul has been instrumental in numerous leading research initia tives and has achieved an unprecedented level of success in facilitating com munication among researchers in diverse disciplines from around the world. A particularly remarkable and unique aspect of his work is that he has been a leading scholar in such diverse areas of research as graph theory and net work models, reliability theory, stochastic optimization, operations research, probability theory, sampling theory, cluster analysis, scaling and multivariate data analysis. His activities have been directed not only at these and other theoretical topics, but also at applications of statistical and mathematical tools to a multitude of important problems in computer science (e.g., w- mining), business research (e.g., market segmentation), management science (e.g., decision support systems) and behavioral sciences (e.g., preference mea surement and data mining). All of his endeavors have been accomplished at the highest level of professional excellence Nota de contenido: Data Analysis -- Optimization in Symbolic Data Analysis: Dissimilarities, Class Centers, and Clustering -- An Efficient Branch and Bound Procedure for Restricted Principal Components Analysis -- A Tree Structured Classifier for Symbolic Class Description -- A Diversity Measure for Tree-Based Classifier Ensembles -- Repeated Confidence Intervals in Self-Organizing Studies -- Fuzzy and Crisp Mahalanobis Fixed Point Clusters -- Interpretation Aids for Multilayer Perceptron Neural Nets -- An Unfolding Scaling Model for Aggregated Preferential Choice Data -- Model-Based Clustering — Discussion on Some Approaches -- Three-Way Multidimensional Scaling: Formal Properties and Relationships Between Scaling Methods -- Empirical Approach as a Scientific Framework for Data Analysis -- Asymmetric Multidimensional Scaling of Relationships Among Managers of a Firm -- Aggregation of Ordinal Judgements Based on Condorcet’s Majority Rule -- ANOVA Models with Generalized Inverses -- Patterns in Search Queries -- Performance Drivers for Depth-First Frequent Pattern Mining -- On the Performance of Algorithms for Two-Mode Hierarchical Cluster Analysis — Results from a Monte Carlo Simulation Study -- Clustering Including Dimensionality Reduction -- The Number of Clusters in Market Segmentation -- On Variability of Optimal Policies in Markov Decision Processes -- Decision Support -- Linking Quality Function Deployment and Conjoint Analysis for New Product Design -- Financial Management in an International Company: An OR-Based Approach for a Logistics Service Provider -- Development of a Long-Term Strategy for the Moscow Urban Transport System -- The Importance of E-Commerce in China and Russia — An Empirical Comparison -- Analyzing Trading Behavior in Transaction Data of Electronic Election Markets -- Critical Success Factors for Data Mining Projects -- Equity Analysis by Functional Approach -- A Multidimensional Approach to Country of Origin Effects in the Automobile Market -- Loyalty Programs and Their Impact on Repeat Purchase Behaviour: An Extension on the “Single Source” Panel BehaviorScan -- An Empirical Examination of Daily Stock Return Distributions for U.S. Stocks -- Stages, Gates, and Conflicts in New Product Development: A Classification Approach -- Analytical Lead Management in the Automotive Industry -- Die Nutzung von multivariaten statistischen Verfahren in der Praxis - Ein Erfahrungsbericht 20 Jahre danach -- Heuristic Bundling -- The Option of No-Purchase in the Empirical Description of Brand Choice Behaviour -- klaR Analyzing German Business Cycles En línea: http://dx.doi.org/10.1007/3-540-28397-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35297 Data Analysis and Decision Support [documento electrónico] / SpringerLink (Online service) ; Daniel Baier ; Reinhold Decker ; Schmidt-Thieme, Lars . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2005 . - XI, 352 p : online resource. - (Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814) .
ISBN : 978-3-540-28397-3
Idioma : Inglés (eng)
Palabras clave: Statistics Information technology Business Data processing structures (Computer science) Mathematical statistics Computers Statistical Theory and Methods IT in Probability Computer Science Systems Communication Service Structures Clasificación: 51 Matemáticas Resumen: It is a great privilege and pleasure to write a foreword for a book honor ing Wolfgang Gaul on the occasion of his sixtieth birthday. Wolfgang Gaul is currently Professor of Business Administration and Management Science and the Head of the Institute of Decision Theory and Management Science, Faculty of Economics, University of Karlsruhe (TH), Germany. He is, by any measure, one of the most distinguished and eminent scholars in the world today. Wolfgang Gaul has been instrumental in numerous leading research initia tives and has achieved an unprecedented level of success in facilitating com munication among researchers in diverse disciplines from around the world. A particularly remarkable and unique aspect of his work is that he has been a leading scholar in such diverse areas of research as graph theory and net work models, reliability theory, stochastic optimization, operations research, probability theory, sampling theory, cluster analysis, scaling and multivariate data analysis. His activities have been directed not only at these and other theoretical topics, but also at applications of statistical and mathematical tools to a multitude of important problems in computer science (e.g., w- mining), business research (e.g., market segmentation), management science (e.g., decision support systems) and behavioral sciences (e.g., preference mea surement and data mining). All of his endeavors have been accomplished at the highest level of professional excellence Nota de contenido: Data Analysis -- Optimization in Symbolic Data Analysis: Dissimilarities, Class Centers, and Clustering -- An Efficient Branch and Bound Procedure for Restricted Principal Components Analysis -- A Tree Structured Classifier for Symbolic Class Description -- A Diversity Measure for Tree-Based Classifier Ensembles -- Repeated Confidence Intervals in Self-Organizing Studies -- Fuzzy and Crisp Mahalanobis Fixed Point Clusters -- Interpretation Aids for Multilayer Perceptron Neural Nets -- An Unfolding Scaling Model for Aggregated Preferential Choice Data -- Model-Based Clustering — Discussion on Some Approaches -- Three-Way Multidimensional Scaling: Formal Properties and Relationships Between Scaling Methods -- Empirical Approach as a Scientific Framework for Data Analysis -- Asymmetric Multidimensional Scaling of Relationships Among Managers of a Firm -- Aggregation of Ordinal Judgements Based on Condorcet’s Majority Rule -- ANOVA Models with Generalized Inverses -- Patterns in Search Queries -- Performance Drivers for Depth-First Frequent Pattern Mining -- On the Performance of Algorithms for Two-Mode Hierarchical Cluster Analysis — Results from a Monte Carlo Simulation Study -- Clustering Including Dimensionality Reduction -- The Number of Clusters in Market Segmentation -- On Variability of Optimal Policies in Markov Decision Processes -- Decision Support -- Linking Quality Function Deployment and Conjoint Analysis for New Product Design -- Financial Management in an International Company: An OR-Based Approach for a Logistics Service Provider -- Development of a Long-Term Strategy for the Moscow Urban Transport System -- The Importance of E-Commerce in China and Russia — An Empirical Comparison -- Analyzing Trading Behavior in Transaction Data of Electronic Election Markets -- Critical Success Factors for Data Mining Projects -- Equity Analysis by Functional Approach -- A Multidimensional Approach to Country of Origin Effects in the Automobile Market -- Loyalty Programs and Their Impact on Repeat Purchase Behaviour: An Extension on the “Single Source” Panel BehaviorScan -- An Empirical Examination of Daily Stock Return Distributions for U.S. Stocks -- Stages, Gates, and Conflicts in New Product Development: A Classification Approach -- Analytical Lead Management in the Automotive Industry -- Die Nutzung von multivariaten statistischen Verfahren in der Praxis - Ein Erfahrungsbericht 20 Jahre danach -- Heuristic Bundling -- The Option of No-Purchase in the Empirical Description of Brand Choice Behaviour -- klaR Analyzing German Business Cycles En línea: http://dx.doi.org/10.1007/3-540-28397-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35297 Ejemplares
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Título : Data Quality for Decision Makers : A dialog between a board member and a DQ expert Tipo de documento: documento electrónico Autores: Guilherme Morbey ; SpringerLink (Online service) Editorial: Wiesbaden : Springer Fachmedien Wiesbaden Fecha de publicación: 2013 Otro editor: Imprint: Springer Gabler Número de páginas: XI, 78 p. 7 illus Il.: online resource ISBN/ISSN/DL: 978-3-658-01823-8 Idioma : Inglés (eng) Palabras clave: Business Information technology Data processing and Management IT in Clasificación: 658 Empresas. Organización de empresas Resumen: Currently many companies are confronted with the decision how to deal with the new data quality requirements of the regulatory authorities. Future data quality statements for enterprise key figures and their origins are being demanded. Applying methods of a data quality management system can produce these statements best. Guilherme Morbey explains the introduction of such a system in the form of a dialogue. Content · Data quality in general · Organisational and technical requirements · Stumbling blocks · Factors for Success Target groups · Faculty members and students in business administration focussing on corporate governance and business computer science · Managers and upcoming professionals in business The Author Guilherme Morbey, owner of Morbey Consulting, is primarily engaged in financial services. One of his main topics is the introduction of data quality management systems. Nota de contenido: Data quality in general -- Organisational and technical requirements -- Stumbling blocks.- Factors for Success En línea: http://dx.doi.org/10.1007/978-3-658-01823-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=36666 Data Quality for Decision Makers : A dialog between a board member and a DQ expert [documento electrónico] / Guilherme Morbey ; SpringerLink (Online service) . - Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Gabler, 2013 . - XI, 78 p. 7 illus : online resource.
ISBN : 978-3-658-01823-8
Idioma : Inglés (eng)
Palabras clave: Business Information technology Data processing and Management IT in Clasificación: 658 Empresas. Organización de empresas Resumen: Currently many companies are confronted with the decision how to deal with the new data quality requirements of the regulatory authorities. Future data quality statements for enterprise key figures and their origins are being demanded. Applying methods of a data quality management system can produce these statements best. Guilherme Morbey explains the introduction of such a system in the form of a dialogue. Content · Data quality in general · Organisational and technical requirements · Stumbling blocks · Factors for Success Target groups · Faculty members and students in business administration focussing on corporate governance and business computer science · Managers and upcoming professionals in business The Author Guilherme Morbey, owner of Morbey Consulting, is primarily engaged in financial services. One of his main topics is the introduction of data quality management systems. Nota de contenido: Data quality in general -- Organisational and technical requirements -- Stumbling blocks.- Factors for Success En línea: http://dx.doi.org/10.1007/978-3-658-01823-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=36666 Ejemplares
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Título : Data Analysis, Machine Learning and Applications : Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Christine Preisach ; Hans Burkhardt ; Schmidt-Thieme, Lars ; Reinhold Decker Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2008 Colección: Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814 Número de páginas: XVI, 719 p Il.: online resource ISBN/ISSN/DL: 978-3-540-78246-9 Idioma : Inglés (eng) Palabras clave: Computer science Information technology Business Data processing mining Artificial intelligence Statistics Science Intelligence (incl. Robotics) Statistics, general IT in Mining and Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007 Nota de contenido: Classification -- Clustering -- Multidimensional Data Analysis -- Analysis of Complex Data -- Exploratory Data Analysis and Tools for Data Analysis -- Marketing and Management Science -- Banking and Finance -- Business Intelligence -- Text Mining, Web Mining, and the Semantic Web -- Linguistics -- Data Analysis in Humanities En línea: http://dx.doi.org/10.1007/978-3-540-78246-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34372 Data Analysis, Machine Learning and Applications : Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007 [documento electrónico] / SpringerLink (Online service) ; Christine Preisach ; Hans Burkhardt ; Schmidt-Thieme, Lars ; Reinhold Decker . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2008 . - XVI, 719 p : online resource. - (Studies in Classification, Data Analysis, and Knowledge Organization, ISSN 1431-8814) .
ISBN : 978-3-540-78246-9
Idioma : Inglés (eng)
Palabras clave: Computer science Information technology Business Data processing mining Artificial intelligence Statistics Science Intelligence (incl. Robotics) Statistics, general IT in Mining and Knowledge Discovery Clasificación: 51 Matemáticas Resumen: Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007 Nota de contenido: Classification -- Clustering -- Multidimensional Data Analysis -- Analysis of Complex Data -- Exploratory Data Analysis and Tools for Data Analysis -- Marketing and Management Science -- Banking and Finance -- Business Intelligence -- Text Mining, Web Mining, and the Semantic Web -- Linguistics -- Data Analysis in Humanities En línea: http://dx.doi.org/10.1007/978-3-540-78246-9 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34372 Ejemplares
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Título : Data Correcting Approaches in Combinatorial Optimization Tipo de documento: documento electrónico Autores: Boris I. Goldengorin ; SpringerLink (Online service) ; Panos M. Pardalos Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: SpringerBriefs in Optimization, ISSN 2190-8354 Número de páginas: X, 114 p. 41 illus Il.: online resource ISBN/ISSN/DL: 978-1-4614-5286-7 Idioma : Inglés (eng) Palabras clave: Mathematics Data structures (Computer science) Algorithms Mathematical optimization Graph theory Theory Optimization Structures Algorithm Analysis and Problem Complexity Clasificación: 51 Matemáticas Resumen: Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems En línea: http://dx.doi.org/10.1007/978-1-4614-5286-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32852 Data Correcting Approaches in Combinatorial Optimization [documento electrónico] / Boris I. Goldengorin ; SpringerLink (Online service) ; Panos M. Pardalos . - New York, NY : Springer New York : Imprint: Springer, 2012 . - X, 114 p. 41 illus : online resource. - (SpringerBriefs in Optimization, ISSN 2190-8354) .
ISBN : 978-1-4614-5286-7
Idioma : Inglés (eng)
Palabras clave: Mathematics Data structures (Computer science) Algorithms Mathematical optimization Graph theory Theory Optimization Structures Algorithm Analysis and Problem Complexity Clasificación: 51 Matemáticas Resumen: Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems En línea: http://dx.doi.org/10.1007/978-1-4614-5286-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32852 Ejemplares
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Título : Data Mining for Biomarker Discovery Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Panos M. Pardalos ; Petros Xanthopoulos ; Michalis Zervakis Editorial: Boston, MA : Springer US Fecha de publicación: 2012 Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 65 Número de páginas: XIV, 246 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-2107-8 Idioma : Inglés (eng) Palabras clave: Mathematics Biochemical engineering Health informatics Data mining Operations research Management science Research, Science Mining and Knowledge Discovery Informatics Engineering Clasificación: 51 Matemáticas Resumen: Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms Nota de contenido: Preface -- 1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang) -- Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis) -- 3. EEG Features as Biomarkers for Discrimination of Pre-ictal states (A. Tsimpiris, D. Kugiumtzis) -- 4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non-epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D.-S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos) -- 5. Classification of Tree and Network Topology Structures in Medical Images (A. Skoura, V. Megalooikonomou, A. Diamantopolous, G.C. Kagadis, D. Karnabatidis) -- 6. A Framework for Multi-Modal Imagin Biomarker Extraction with Application to Brain MRI (K. Maria, V. Sakkalis, N. Graf) -- 7. A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data (E. Tsolaki, E. Kousi, E. Kapsalaki, I. Dimou, K. Theodorou, G. C. Manikis, C. Kappas, I. Tsougos) -- 8. Data Mining for Cancer Biomarkers with Raman Spectroscopy (M.B.Fenn, V. Pappu) -- 9. Nonlinear Recognition Methods for Oncological Pathologies (G. Patrizi, V. Pietropaolo, A. Carbone, R. De Leone, L. Di Giacomo, V. Losaco, G. Patrizi) -- 10. Studying Connectivity Properties in Human Protein Interation Network in Cancer Pathway (V. Tomaino, A. Arulselvan, P. Veltri, P.M. Pardalos) -- 11. Modeling of Oral Cancer Progression Using Dynamic Bayesian Networks (K.P. Exarchos, G. Rigas, Y. Golestsis, D.I. Fotiadis) -- 12. Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS Radiological and Histopathological Findings (P. Drakatos, D. Lykouras, F. Sampsonas, K. Karkoulias, K. Spiropoulos) -- 13. Data Mining System Applied to Population Databases for Studies on Lung Cancer (J. Pérez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano) En línea: http://dx.doi.org/10.1007/978-1-4614-2107-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32782 Data Mining for Biomarker Discovery [documento electrónico] / SpringerLink (Online service) ; Panos M. Pardalos ; Petros Xanthopoulos ; Michalis Zervakis . - Boston, MA : Springer US, 2012 . - XIV, 246 p : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 65) .
ISBN : 978-1-4614-2107-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Biochemical engineering Health informatics Data mining Operations research Management science Research, Science Mining and Knowledge Discovery Informatics Engineering Clasificación: 51 Matemáticas Resumen: Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms Nota de contenido: Preface -- 1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang) -- Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis) -- 3. EEG Features as Biomarkers for Discrimination of Pre-ictal states (A. Tsimpiris, D. Kugiumtzis) -- 4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non-epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D.-S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos) -- 5. Classification of Tree and Network Topology Structures in Medical Images (A. Skoura, V. Megalooikonomou, A. Diamantopolous, G.C. Kagadis, D. Karnabatidis) -- 6. A Framework for Multi-Modal Imagin Biomarker Extraction with Application to Brain MRI (K. Maria, V. Sakkalis, N. Graf) -- 7. A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data (E. Tsolaki, E. Kousi, E. Kapsalaki, I. Dimou, K. Theodorou, G. C. Manikis, C. Kappas, I. Tsougos) -- 8. Data Mining for Cancer Biomarkers with Raman Spectroscopy (M.B.Fenn, V. Pappu) -- 9. Nonlinear Recognition Methods for Oncological Pathologies (G. Patrizi, V. Pietropaolo, A. Carbone, R. De Leone, L. Di Giacomo, V. Losaco, G. Patrizi) -- 10. Studying Connectivity Properties in Human Protein Interation Network in Cancer Pathway (V. Tomaino, A. Arulselvan, P. Veltri, P.M. Pardalos) -- 11. Modeling of Oral Cancer Progression Using Dynamic Bayesian Networks (K.P. Exarchos, G. Rigas, Y. Golestsis, D.I. Fotiadis) -- 12. Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS Radiological and Histopathological Findings (P. Drakatos, D. Lykouras, F. Sampsonas, K. Karkoulias, K. Spiropoulos) -- 13. Data Mining System Applied to Population Databases for Studies on Lung Cancer (J. Pérez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano) En línea: http://dx.doi.org/10.1007/978-1-4614-2107-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32782 Ejemplares
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