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Neural Networks and Statistical Learning

Neural Networks and Statistical Learning PDF Author: Ke-Lin Du
Publisher: Springer Nature
ISBN: 1447174526
Category : Mathematics
Languages : en
Pages : 988

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Book Description
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.

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning PDF Author: Ke-Lin Du
Publisher: Springer Nature
ISBN: 1447174526
Category : Mathematics
Languages : en
Pages : 988

View

Book Description
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

Statistical Learning Using Neural Networks PDF Author: Basilio de Braganca Pereira
Publisher: CRC Press
ISBN: 0429775555
Category : Business & Economics
Languages : en
Pages : 234

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Book Description
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.

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning PDF Author: K.-L. Du
Publisher:
ISBN: 9781447174530
Category : Computational intelligence
Languages : en
Pages : 988

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Book Description
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

Statistical Learning Using Neural Networks PDF Author: Basilio de Braganca Pereira
Publisher: CRC Press
ISBN: 0429775547
Category : Business & Economics
Languages : en
Pages : 234

View

Book Description
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 With Artificial Neural Network Applied To Health And Environmental Data

Statistical Learning With Artificial Neural Network Applied To Health And Environmental Data PDF Author: Taysseer Sharaf
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages :

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Book Description
The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using artificial neural network. We present a comparison between the two methodology and update one of them to estimate survival time of competing risks. To fit a model using artificial neural network, you need to take care of two parts; first one is the neural network architecture and second part is the learning algorithm. Usually neural networks are trained using a non-linear optimization algorithm such as quasi Newton Raphson algorithm. Other learning algorithms are base on Bayesian inference. In this study we present a new learning technique by using a mixture of the two available methodologies for using Bayesian inference in training of neural networks. We have performed our analysis using real world data. We have used patients diagnosed with skin cancer in the United states from SEER database, under the supervision of the National Cancer Institute The second part of this dissertation presents the utilization of artificial neural to time series analysis. We present a new method of training recurrent artificial neural network with Hybrid Monte Carlo Sampling and compare our findings with the popular auto-regressive integrated moving average (ARIMA) model. We used the carbon dioxide monthly average emission to apply our comparison, data collected from NOAA.

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III PDF Author: Michel Denuit
Publisher: Springer Nature
ISBN: 3030258270
Category : Business & Economics
Languages : en
Pages : 250

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Book Description
This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third 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.

Neural Networks for Intelligent Signal Processing

Neural Networks for Intelligent Signal Processing PDF Author: Anthony Zaknich
Publisher: World Scientific
ISBN: 9812383050
Category : Technology & Engineering
Languages : en
Pages : 484

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Book Description
This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.

Effective Statistical Learning Methods for Actuaries

Effective Statistical Learning Methods for Actuaries PDF Author: Michel Denuit
Publisher:
ISBN: 9783030258283
Category : Actuarial science
Languages : en
Pages :

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Book Description
Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.

The Nature of Statistical Learning Theory

The Nature of Statistical Learning Theory PDF Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 314

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Book Description
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.

Statistical Learning with Neural Networks Trained by Gradient Descent

Statistical Learning with Neural Networks Trained by Gradient Descent PDF Author: Spencer Frei
Publisher:
ISBN:
Category :
Languages : en
Pages : 206

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Book Description
In this thesis, we theoretically analyze the ability of neural networks trained by gradient descent to learn. The learning problem consists of an algorithmic component and a statistical component. The algorithmic question concerns the underlying optimization problem: given samples from a distribution, under what conditions can a neural network trained by gradient descent efficiently minimize the empirical risk for some loss function defined over these samples? As the underlying optimization problem is highly non-convex, standard tools from optimization theory are not applicable and thus a novel analysis is needed. The statistical question concerns the generalization problem: supposing gradient descent is successful at minimizing the empirical risk, under what conditions does this translate to a guarantee for the population risk? Contemporary neural networks used in practice are highly overparameterized and are capable of minimizing the empirical risk even when the true labels are replaced with random noise, and thus standard uniform convergence-based arguments will fail to yield meaningful guarantees for the population risk for these models. We begin our thesis by analyzing the simplest nontrivial neural network possible: a single neuron with a nonlinear activation function under the squared loss. Even this simple network induces a highly non-convex optimization problem. By showing that an approximate surrogate risk is minimized throughout the gradient descent trajectory, we show that gradient descent is able to learn single neurons for a large class of nonlinear activation functions. Our results hold in the agnostic setting, implying that gradient descent succeeds even when the model is mis-specified. We continue our analysis of the single neuron by examining the classification setting, where the loss of interest is the zero-one loss rather than the squared loss. As the decision boundary for single neurons in the classification setting is identical to that of linear classifiers for typical activation functions, we focus on the linear classifier setting. This reduces the problem to that of learning halfspaces with noise, a long-studied problem in computational learning theory with well-established computational hardness constraints on the learning problem due to the non-convexity of the zero-one loss. We establish connections between minimizers of convex surrogates of the zero-one loss and minimizers of the zero-one loss itself to develop the first positive guarantees for gradient descent on convex loss functions for learning halfspaces with agnostic noise. We then establish guarantees for learning halfspaces with agnostic noise when using overparameterized SGD-trained two layer nonlinear neural networks. Our analysis requires both overcoming the non-convexity of the underlying optimization problem as well as avoiding generalization bounds that become vacuous when the number of parameters in the neural network becomes large. In our final contribution, we derive generalization bounds for overparameterized deep residual networks trained by gradient descent. Our techniques leverage a recently developed correspondence between large, overparameterized neural networks and the tangent kernels of their infinite width approximations known as the neural tangent kernel.