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Generalization In Feedforward Neural Networks
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Book Synopsis Generalization in feedforward neural networks by : Darrell Whitley
Download or read book Generalization in feedforward neural networks written by Darrell Whitley and published by . This book was released on 1991 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "One of the important characteristics of feed forward neural networks is their ability to generalize the input/output behavior of functions based on a set of training exemplars. Yet many aspects of the problem of improving generalization in feed forward neural networks has [sic] not been studied well. In this paper we address the importance of this problem and propose two techniques to improve generalization. They are: 1) proper selection of the training ensemble, and 2) a partitioned learning strategy. These techniques are applied to a complex 2-D classification problem. We also evaluate network generalization while using the cascade correlation learning architecture."
Book Synopsis Learning and Generalization in Feed-forward Neural Networks by : Frank J. Smieja
Download or read book Learning and Generalization in Feed-forward Neural Networks written by Frank J. Smieja and published by . This book was released on 1989 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theoretical Aspects of Generalization in Feed-forward Neural Networks by : Keith Richard Potter
Download or read book Theoretical Aspects of Generalization in Feed-forward Neural Networks written by Keith Richard Potter and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Assessing Generalization of Feedforward Neural Networks by : Michael J. Turmon
Download or read book Assessing Generalization of Feedforward Neural Networks written by Michael J. Turmon and published by . This book was released on 1995 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Some Aspects of Generalization in Feed-forward Artificial Neural Networks by : Russell D. Reed
Download or read book Some Aspects of Generalization in Feed-forward Artificial Neural Networks written by Russell D. Reed and published by . This book was released on 1995 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Feedforward Neural Network Methodology by : Terrence L. Fine
Download or read book Feedforward Neural Network Methodology written by Terrence L. Fine and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
Download or read book Neural Smithing written by Russell Reed and published by MIT Press. This book was released on 1999-02-17 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
Book Synopsis Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks by : Robert B. Pinter
Download or read book Nonlinear Vision: Determination of Neural Receptive Fields, Function, and Networks written by Robert B. Pinter and published by CRC Press. This book was released on 2018-05-04 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text brings to vision research a treatment different from that often found in books on the subject in its emphasis on nonlinear aspects of vision, from human perception to eye cells of the fly. There is considerable emphasis on mathematics, which forms not only models but the algorithms for processing data.
Book Synopsis A Study of Scaling and Generalization in Neural Networks by : Subutai Ahmad
Download or read book A Study of Scaling and Generalization in Neural Networks written by Subutai Ahmad and published by . This book was released on 1988 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Information-theoretic Perspectives on Generalization and Robustness of Neural Networks by : Adrian Tovar Lopez
Download or read book Information-theoretic Perspectives on Generalization and Robustness of Neural Networks written by Adrian Tovar Lopez and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks as efficient as they are in practice, remain in several aspects still a mystery. Some of the most studied questions are: where does their generalization capabilities come from? What are the reason behind the existence of adversarial examples? In this thesis I use a formal mathematical representation of neural networks to investigate this questions. I also develop new algorithms based on the theory developed. The first par of the thesis is concerned with the generalization error which characterizes the gap between an algorithm's performance on test data versus performance on training data. I derive upper bounds on the generalization error in terms of a certain Wasserstein distance involving the distributions of input and the output under the assumption of a Lipschitz continuous loss function. Unlike mutual information-based bounds, these new bounds are useful for algorithms such as stochastic gradient descent. Moreover, I show that in some natural cases these bounds are tighter than mutual information-based bounds. In the second part of the thesis I study manifold learning. The goal is to learn a manifold that captures the inherent low-dimensionality of high-dimensional data. I present a novel training procedure to learn manifolds using neural networks. Parametrizing the manifold via a neural network with a low-dimensional input and a high-dimensional output. During training, I calculate the distance between the training data points and the manifold via a geometric projection and update the network weights so that this distance diminishes. The learned manifold is seen to interpolate the training data, analogous to autoencoders. Experiments show that the procedure leads to lower reconstruction errors for noisy inputs, and higher adversarial accuracy when used in manifold defense methods than those of autoencoders. In the final part of the thesis I propose an information bottleneck principle for causal time-series prediction. I develop variational bounds on the information bottleneck objective function that can be efficiently optimized using recurrent neural networks. Then implement an algorithm on simulated data as well as real-world weather-prediction and stock market-prediction datasets and show that these problems can be successfully solved using the new information bottleneck principle.
Book Synopsis Neural Networks with R by : Giuseppe Ciaburro
Download or read book Neural Networks with R written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-09-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Mathematics Of Generalization by : David. H Wolpert
Download or read book The Mathematics Of Generalization written by David. H Wolpert and published by CRC Press. This book was released on 2018-03-05 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
Book Synopsis A Generalized View on Learning in Feedforward Neural Networks by : Georg Dorffner
Download or read book A Generalized View on Learning in Feedforward Neural Networks written by Georg Dorffner and published by . This book was released on 1995 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Generalization and Neural Networks by : Forest Dan Foresee
Download or read book Generalization and Neural Networks written by Forest Dan Foresee and published by . This book was released on 1996 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Generalization of Wide Neural Networks from the Perspective of Linearization and Kernel Learning by : Hui Jin
Download or read book Generalization of Wide Neural Networks from the Perspective of Linearization and Kernel Learning written by Hui Jin and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently people showed that wide neural networks can be approximated by linear models under gradient descent [JGH18a,LXS19a]. In this dissertation we study generalization of wide neural networks by the linearization of the network, thus some result from kernel learning can directly apply [SH02,CD07]. In Chapter 2, we investigate gradient descent training of wide neural networks and the corresponding implicit bias in function space. We approximate the wide neural networks by corresponding linearized models and show that the implicit bias can be characterized by certain interpolating splines, thus we can use the approximation theory of splines to study the generalization of wide neural networks. In Chapter 3, we show that the decay rate of generalization error of Gaussian Process Regression is determined by the decay rate of the eigenspectrum of the prior and the eigenexpansion coefficients of the target function. This result can be applied to study the generalization error of infinitely wide neural networks with ReLU activations. Since the asymptotic generalization error is closely related to the asymptotic spectrum of the kernel, in Chapter 4 we study the asymptotic spectrum of the Neural Tangent Kernel (NTK) by its power series expansion. We first show that under certain assumptions, the NTK of deep feedforward networks in the infinite width limit can be expressed as a power series. Later on we show that the eigenvalues of the NTK can be expressed the coefficients of the power series. From this expression we show that the decay rate of the eigenvalues is determined by the decay rate of the power series coefficients.
Book Synopsis Process Neural Networks by : Xingui He
Download or read book Process Neural Networks written by Xingui He and published by Springer Science & Business Media. This book was released on 2010-07-05 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.
Book Synopsis Neural Networks: Tricks of the Trade by : Grégoire Montavon
Download or read book Neural Networks: Tricks of the Trade written by Grégoire Montavon and published by Springer. This book was released on 2012-11-14 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
