Author: Calyampudi Radhakrishna Rao
Publisher: Taylor & Francis
ISBN: 9788122412857
Category : Mathematical statistics
Languages : en
Pages : 520
Book Description
The Volume Five Of Selected Papers Of C.R. Rao Consists Of 32 Papers That Appeared In Various Publications From 1985. These Papers Are Selected To Showcase Some Of The Fundamental Contributions In Characterizations Of Probability Distributions, Density Estimation, Analysis Of Multivariate Familial Data, Correspondence Analysis, Shape And Size Analysis, Signal Detection, Inference Based On Quadratic Entropy, Bootstrap, L-L Norm, Convex Discrepancy Function Etc., Estimation Problems In Univariate And Multivariate Linear Models And Regression Models Using Unified Theory Of Linear Estimation, M-Estimates, Lad Estimates Etc. And Many More Novel Concepts And Ideas With Enormous Potential For Further Research And In Which Active Research Is Being Carried Out.The Highlight Of This Volume Is The Stimulating Retrospection Of Prof. C.R. Rao About His Work Spanning The Last Three Score Years. An Updated Bibliography And A Brief Biographical Profile Of Prof. Rao Are Also Included.These Volumes Are Intended Not Only As A Ready Reference To Most Of Prof. Rao'S Oft Quoted And Used Results But Also To Inspire And Initiate Research Workers To The Broad Spectrum Of Areas In Theoretical And Applied Statistics In Which Prof. Rao Has Contributed.
Selected Papers of C.R. Rao
Author: Calyampudi Radhakrishna Rao
Publisher: Taylor & Francis
ISBN: 9788122412857
Category : Mathematical statistics
Languages : en
Pages : 520
Book Description
The Volume Five Of Selected Papers Of C.R. Rao Consists Of 32 Papers That Appeared In Various Publications From 1985. These Papers Are Selected To Showcase Some Of The Fundamental Contributions In Characterizations Of Probability Distributions, Density Estimation, Analysis Of Multivariate Familial Data, Correspondence Analysis, Shape And Size Analysis, Signal Detection, Inference Based On Quadratic Entropy, Bootstrap, L-L Norm, Convex Discrepancy Function Etc., Estimation Problems In Univariate And Multivariate Linear Models And Regression Models Using Unified Theory Of Linear Estimation, M-Estimates, Lad Estimates Etc. And Many More Novel Concepts And Ideas With Enormous Potential For Further Research And In Which Active Research Is Being Carried Out.The Highlight Of This Volume Is The Stimulating Retrospection Of Prof. C.R. Rao About His Work Spanning The Last Three Score Years. An Updated Bibliography And A Brief Biographical Profile Of Prof. Rao Are Also Included.These Volumes Are Intended Not Only As A Ready Reference To Most Of Prof. Rao'S Oft Quoted And Used Results But Also To Inspire And Initiate Research Workers To The Broad Spectrum Of Areas In Theoretical And Applied Statistics In Which Prof. Rao Has Contributed.
Publisher: Taylor & Francis
ISBN: 9788122412857
Category : Mathematical statistics
Languages : en
Pages : 520
Book Description
The Volume Five Of Selected Papers Of C.R. Rao Consists Of 32 Papers That Appeared In Various Publications From 1985. These Papers Are Selected To Showcase Some Of The Fundamental Contributions In Characterizations Of Probability Distributions, Density Estimation, Analysis Of Multivariate Familial Data, Correspondence Analysis, Shape And Size Analysis, Signal Detection, Inference Based On Quadratic Entropy, Bootstrap, L-L Norm, Convex Discrepancy Function Etc., Estimation Problems In Univariate And Multivariate Linear Models And Regression Models Using Unified Theory Of Linear Estimation, M-Estimates, Lad Estimates Etc. And Many More Novel Concepts And Ideas With Enormous Potential For Further Research And In Which Active Research Is Being Carried Out.The Highlight Of This Volume Is The Stimulating Retrospection Of Prof. C.R. Rao About His Work Spanning The Last Three Score Years. An Updated Bibliography And A Brief Biographical Profile Of Prof. Rao Are Also Included.These Volumes Are Intended Not Only As A Ready Reference To Most Of Prof. Rao'S Oft Quoted And Used Results But Also To Inspire And Initiate Research Workers To The Broad Spectrum Of Areas In Theoretical And Applied Statistics In Which Prof. Rao Has Contributed.
Index of Conference Proceedings
Author: British Library. Document Supply Centre
Publisher:
ISBN:
Category : Conference proceedings
Languages : en
Pages : 870
Book Description
Publisher:
ISBN:
Category : Conference proceedings
Languages : en
Pages : 870
Book Description
Mathematical Reviews
Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1518
Book Description
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1518
Book Description
Proceedings of the Fourth SIAM International Conference on Data Mining
Author: Michael W. Berry
Publisher: SIAM
ISBN: 9780898715682
Category : Mathematics
Languages : en
Pages : 556
Book Description
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
Publisher: SIAM
ISBN: 9780898715682
Category : Mathematics
Languages : en
Pages : 556
Book Description
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
Nonnegative Matrix and Tensor Factorizations
Author: Andrzej Cichocki
Publisher: John Wiley & Sons
ISBN: 9780470747285
Category : Science
Languages : en
Pages : 500
Book Description
This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area. Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms. Provides a comparative analysis of the different methods in order to identify approximation error and complexity. Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book. The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.
Publisher: John Wiley & Sons
ISBN: 9780470747285
Category : Science
Languages : en
Pages : 500
Book Description
This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area. Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms. Provides a comparative analysis of the different methods in order to identify approximation error and complexity. Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book. The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.
AMSTAT News
Author: American Statistical Association
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 504
Book Description
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 504
Book Description
High Performance Computing for Computational Science -- VECPAR 2010
Author: José M. Laginha M. Palma
Publisher: Springer Science & Business Media
ISBN: 3642193277
Category : Computers
Languages : en
Pages : 483
Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on High Performance Computing for Computational Science, VECPAR 2010, held in Berkeley, CA, USA, in June 2010. The 34 revised full papers presented together with five invited contributions were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on linear algebra and solvers on emerging architectures, large-scale simulations, parallel and distributed computing, numerical algorithms.
Publisher: Springer Science & Business Media
ISBN: 3642193277
Category : Computers
Languages : en
Pages : 483
Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on High Performance Computing for Computational Science, VECPAR 2010, held in Berkeley, CA, USA, in June 2010. The 34 revised full papers presented together with five invited contributions were carefully selected during two rounds of reviewing and revision. The papers are organized in topical sections on linear algebra and solvers on emerging architectures, large-scale simulations, parallel and distributed computing, numerical algorithms.
Advances in Self-Organizing Maps and Learning Vector Quantization
Author: Erzsébet Merényi
Publisher: Springer
ISBN: 3319285181
Category : Technology & Engineering
Languages : en
Pages : 353
Book Description
This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.
Publisher: Springer
ISBN: 3319285181
Category : Technology & Engineering
Languages : en
Pages : 353
Book Description
This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.
Local Pattern Detection
Author: Katharina Morik
Publisher: Springer
ISBN: 3540318941
Category : Computers
Languages : en
Pages : 242
Book Description
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.
Publisher: Springer
ISBN: 3540318941
Category : Computers
Languages : en
Pages : 242
Book Description
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.
Learning from Data Streams
Author: João Gama
Publisher: Springer Science & Business Media
ISBN: 3540736786
Category : Computers
Languages : en
Pages : 486
Book Description
Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.
Publisher: Springer Science & Business Media
ISBN: 3540736786
Category : Computers
Languages : en
Pages : 486
Book Description
Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.