Neural Networks And Statistical Learning

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Neural Networks and Statistical Learning

Ke-Lin Du,M. N. S. Swamy
Neural Networks and Statistical Learning Book PDF

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Science & Business Media
Page : 824 pages
File Size : 54,5 Mb
Release : 2013-12-09
Category : Technology & Engineering
ISBN : 9781447155713

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

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.

Neural Networks and Statistical Learning

Ke-Lin Du,M. N. S. Swamy
Neural Networks and Statistical Learning Book PDF

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Nature
Page : 988 pages
File Size : 42,8 Mb
Release : 2019-09-12
Category : Mathematics
ISBN : 9781447174523

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Statistical Learning Using Neural Networks

Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Statistical Learning Using Neural Networks Book PDF

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 286 pages
File Size : 45,6 Mb
Release : 2020-08-25
Category : Business & Economics
ISBN : 9780429775543

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

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.

Statistical Learning Using Neural Networks

Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Statistical Learning Using Neural Networks Book PDF

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 234 pages
File Size : 44,7 Mb
Release : 2020-09-01
Category : Business & Economics
ISBN : 9780429775550

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

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.

An Introduction to Statistical Learning

Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor
An Introduction to Statistical Learning Book PDF

Author : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor
Publisher : Springer Nature
Page : 617 pages
File Size : 40,5 Mb
Release : 2023-08-01
Category : Mathematics
ISBN : 9783031387470

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An Introduction to Statistical Learning by Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani,Jonathan Taylor Pdf

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Neural Networks and Statistical Learning

K.-L. Du,M. N. S. Swamy
Neural Networks and Statistical Learning Book PDF

Author : K.-L. Du,M. N. S. Swamy
Publisher : Unknown
Page : 988 pages
File Size : 54,6 Mb
Release : 2019
Category : Computational intelligence
ISBN : 1447174534

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Neural Networks and Statistical Learning by K.-L. Du,M. N. S. Swamy Pdf

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models; • clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Statistical Field Theory for Neural Networks

Moritz Helias,David Dahmen
Statistical Field Theory for Neural Networks Book PDF

Author : Moritz Helias,David Dahmen
Publisher : Springer Nature
Page : 203 pages
File Size : 54,7 Mb
Release : 2020-08-20
Category : Science
ISBN : 9783030464448

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Statistical Field Theory for Neural Networks by Moritz Helias,David Dahmen Pdf

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.

Machine Learning with Neural Networks

Bernhard Mehlig
Machine Learning with Neural Networks Book PDF

Author : Bernhard Mehlig
Publisher : Cambridge University Press
Page : 350 pages
File Size : 40,5 Mb
Release : 2021-08-31
Category : Science
ISBN : 1108494935

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Machine Learning with Neural Networks by Bernhard Mehlig Pdf

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

The Elements of Statistical Learning

Trevor Hastie,Robert Tibshirani,Jerome Friedman
The Elements of Statistical Learning Book PDF

Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Page : 545 pages
File Size : 53,7 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387216065

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The Elements of Statistical Learning by Trevor Hastie,Robert Tibshirani,Jerome Friedman Pdf

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

The Nature of Statistical Learning Theory

Vladimir Vapnik
The Nature of Statistical Learning Theory Book PDF

Author : Vladimir Vapnik
Publisher : Springer Science & Business Media
Page : 324 pages
File Size : 42,8 Mb
Release : 2013-06-29
Category : Mathematics
ISBN : 9781475732641

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The Nature of Statistical Learning Theory by Vladimir Vapnik Pdf

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Data Mining Using Neural Networks

Basilio De Braganca Pereira,Calyampudi Radhakrishna Rao,Marepelli Rao
Data Mining Using Neural Networks Book PDF

Author : Basilio De Braganca Pereira,Calyampudi Radhakrishna Rao,Marepelli Rao
Publisher : Chapman & Hall/CRC
Page : 300 pages
File Size : 46,6 Mb
Release : 2012-07-01
Category : Business & Economics
ISBN : 1439875324

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Data Mining Using Neural Networks by Basilio De Braganca Pereira,Calyampudi Radhakrishna Rao,Marepelli Rao Pdf

A concise, easy-to-understand guide to using neural networks in data mining for mathematics, engineering, psychology, and computer science applications, this book compares how neural network models and statistical models are used to tackle data analysis problems. It focuses on the top of the hierarchy of the computational process and shows how neural networks can perform traditional statistical methods of analysis. The book includes some classical and Bayesian statistical inference results and employs R to illustrate the techniques.

Statistical Mechanics of Neural Networks

Haiping Huang
Statistical Mechanics of Neural Networks Book PDF

Author : Haiping Huang
Publisher : Springer Nature
Page : 302 pages
File Size : 46,6 Mb
Release : 2022-01-04
Category : Science
ISBN : 9789811675706

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Statistical Mechanics of Neural Networks by Haiping Huang Pdf

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.

Neural Networks with R

Giuseppe Ciaburro,Balaji Venkateswaran
Neural Networks with R Book PDF

Author : Giuseppe Ciaburro,Balaji Venkateswaran
Publisher : Packt Publishing Ltd
Page : 270 pages
File Size : 40,8 Mb
Release : 2017-09-27
Category : Computers
ISBN : 9781788399418

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Neural Networks with R by Giuseppe Ciaburro,Balaji Venkateswaran Pdf

Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 52,9 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

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Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Effective Statistical Learning Methods for Actuaries I

Michel Denuit,Donatien Hainaut,Julien Trufin
Effective Statistical Learning Methods for Actuaries I Book PDF

Author : Michel Denuit,Donatien Hainaut,Julien Trufin
Publisher : Unknown
Page : 441 pages
File Size : 52,9 Mb
Release : 2019
Category : Actuarial science
ISBN : 3030258211

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Effective Statistical Learning Methods for Actuaries I by Michel Denuit,Donatien Hainaut,Julien Trufin Pdf

This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P & C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.