Neural Networks for Statistical Modeling
Author | : Murray Smith |
Publisher | : Van Nostrand Reinhold Company |
Total Pages | : 268 |
Release | : 1993 |
Genre | : Computers |
ISBN | : |
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Author | : Murray Smith |
Publisher | : Van Nostrand Reinhold Company |
Total Pages | : 268 |
Release | : 1993 |
Genre | : Computers |
ISBN | : |
Author | : Ke-Lin Du |
Publisher | : Springer Science & Business Media |
Total Pages | : 834 |
Release | : 2013-12-09 |
Genre | : Technology & Engineering |
ISBN | : 1447155718 |
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Author | : Murray Smith |
Publisher | : Itp New Media |
Total Pages | : 235 |
Release | : 1996-01-01 |
Genre | : Mathematical statistics |
ISBN | : 9781850328421 |
Author | : Basilio de Braganca Pereira |
Publisher | : CRC Press |
Total Pages | : 286 |
Release | : 2020-08-25 |
Genre | : Business & Economics |
ISBN | : 0429775547 |
Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Author | : Pierre Peretto |
Publisher | : Cambridge University Press |
Total Pages | : 496 |
Release | : 1992-10-29 |
Genre | : Computers |
ISBN | : 9780521424875 |
This book is a beginning graduate-level introduction to neural networks which is divided into four parts.
Author | : Haiping Huang |
Publisher | : Springer Nature |
Total Pages | : 302 |
Release | : 2022-01-04 |
Genre | : Science |
ISBN | : 9811675708 |
This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.
Author | : Herbert K. H. Lee |
Publisher | : SIAM |
Total Pages | : 106 |
Release | : 2004-01-01 |
Genre | : Mathematics |
ISBN | : 9780898718423 |
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Author | : Ron S. Kenett |
Publisher | : John Wiley & Sons |
Total Pages | : 533 |
Release | : 2012-01-30 |
Genre | : Mathematics |
ISBN | : 0470971282 |
Customer survey studies deals with customers, consumers and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays. As demonstrated in this book, integrating such basic analysis with more advanced tools, provides insights on non-obvious patterns and important relationships between the survey variables. This knowledge can significantly affect the conclusions derived from a survey. Key features: Provides an integrated, case-studies based approach to analysing customer survey data. Presents a general introduction to customer surveys, within an organization’s business cycle. Contains classical techniques with modern and non standard tools. Focuses on probabilistic techniques from the area of statistics/data analysis and covers all major recent developments. Accompanied by a supporting website containing datasets and R scripts. Customer survey specialists, quality managers and market researchers will benefit from this book as well as specialists in marketing, data mining and business intelligence fields.
Author | : Moritz Helias |
Publisher | : Springer Nature |
Total Pages | : 203 |
Release | : 2020-08-20 |
Genre | : Science |
ISBN | : 303046444X |
This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.
Author | : Gesellschaft für Klassifikation. Jahrestagung |
Publisher | : Springer |
Total Pages | : 572 |
Release | : 1996-03-18 |
Genre | : Business & Economics |
ISBN | : |
This volume presents methods for the analysis of quantitative and qualitative data, and for the processing and ordering of symbolic or linguistic information. The 45 papers combine methods from exploratory and inferential statistics with mathematical and numerical approaches, investigate conceptual classification and ordering structures and describe recent developments for knowledge-based information systems. The contributions are grouped into seven chapters: 1. Classification and Clustering, 2. Uncertainty and Fuzziness, 3. Methods of Data Analysis and Applications, 4. Statistical Models and Methods, 5. Bayesian Learning, 6. Conceptual Classification, Knowledge Ordering and Information Systems, 7. Linguistics and Dialectometry.