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Author : Alice V. Ling
Publisher :
ISBN 13 :
Total Pages : 252 pages
Book Rating : 4.:/5 (219 download)
Download or read book Techniques for the Improvement of Generalization Capabilities of Neural Networks written by Alice V. Ling and published by . This book was released on 1989 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Zhenghua Chen
Publisher : World Scientific
ISBN 13 : 9811218854
Total Pages : 327 pages
Book Rating : 4.8/5 (112 download)
Download or read book Generalization With Deep Learning: For Improvement On Sensing Capability written by Zhenghua Chen and published by World Scientific. This book was released on 2021-04-07 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
Author : P. Santhi Thilagam
Publisher : Springer
ISBN 13 : 3642292801
Total Pages : 656 pages
Book Rating : 4.6/5 (422 download)
Download or read book Advanced Computing, Networking and Security written by P. Santhi Thilagam and published by Springer. This book was released on 2012-04-02 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the International Conference on Advanced Computing, Networking and Security, ADCONS 2011, held in Surathkal, India, in December 2011. The 73 papers included in this book were carefully reviewed and selected from 289 submissions. The papers are organized in topical sections on distributed computing, image processing, pattern recognition, applied algorithms, wireless networking, sensor networks, network infrastructure, cryptography, Web security, and application security.
Author : Kailash L. Kalantri
Publisher :
ISBN 13 :
Total Pages : 146 pages
Book Rating : 4.:/5 (281 download)
Download or read book Improving the Generalization Ability of Neural Network Classifiers written by Kailash L. Kalantri and published by . This book was released on 1992 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hanan Hassan Ali Adlan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (975 download)
Download or read book Rough Neural Networks Architecture for Improving Generalization in Pattern Recognition written by Hanan Hassan Ali Adlan and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are found to be attractive trainable machines for pattern recognition. The capability of these models to accomodate wide variety and variability of conditions, and the ability functions, make them popular research area. This research focuses on developing hybrid rough neural networks. These novel approaches are assumed to provide superior performance with respect to detection and automatic target recognition. In this thesis, hybrid architecture of rough set theory and neural networks have been investigated, developed, and implemented. The first hybrid approach provides novel neural network referred to as Rough Shared weight Neural Networks (RSNN). It uses the concept of approximation based on rough neurons to feature extraction, and experiences the methodology of weight sharing. The network stages are feature extraction network, and a classification network. The extraction network is composed of rough neurons that accounts for the upper and lower approximations and embeds a membership function to replace ordinary activation functions. The neural network learns the rough set's upper and lower approximations as feature extractors simultaneously with classification. The RSNN implements a novel approximations transform. The basic design for the network is provided together with the learning rules. The architechture provides a novel method to pattern recognition and is expected to be robust to any pattern recognition problem. The second hybrid approach is a two stand alone subsystem, reffered to as Rough Neural Networks (RNN). The extraction network extracts detectors that represent pattern's classes to be supplied to the classification network. It works as a filter for original distilled features based on equivalence relations and rough set reduction, while the second is responsible for classification of the outputs from the first system. The two approaches were applied to image pattern recognition problems. The RSNN was applied to automatic target recognition problem. The data is Synthetic Aperture Radar (SAR) image scenes of tanks, and background. The RSNN provides a methodology for designing nonlinear filters withour prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite imgae. A novel feature extraction algorithm was developed to extract the feature vectors. The alogorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. The performance of the rough backpropagation network is improved compared to backpropagation of the same architecture. The network has been designed to produce detection plane for the desired pattern. The hybrid approaches developed in this thesis provide novel techniques to recognition static and dynamic representation of patterns. In both domains the rough set theory improved generalization of the neural networks paradigms. The methodologies are theoretically robust to any pattern recognition problem, and are proved for image enviroments.
Author : Chooi Mey Kwan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (959 download)
Download or read book Improving Generalization Capability of Neural Networks Through Complexity Regularization written by Chooi Mey Kwan and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Danaipong Chetchotsak
Publisher :
ISBN 13 :
Total Pages : 410 pages
Book Rating : 4.:/5 (528 download)
Download or read book Improving Generalization Capability of Neural Networks Under Conditions of Sparse Data written by Danaipong Chetchotsak and published by . This book was released on 2003 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 575 pages
Book Rating : 4./5 ( download)
Download or read book Better Deep Learning written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2018-12-13 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.
Author : Darrell Whitley
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (257 download)
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."
Download or read book Improving ANN Generalization Via Self-Organized Flocking in Conjunction with Multitasked Backpropagation written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this research has been to develop methods of improving the generalization capabilities of artificial neural networks. Tools for examining the influence of individual training set patterns on the learning abilities of individual neurons are put forth and utilized in the implementation of new network learning algorithms. Algorithms are based largely on the supervised training algorithm: backpropagation, and all experiments use the standard backpropagation algorithm for comparison of results. The focus of the new learning algorithms revolve around the addition of two main components. The first addition is that of an unsupervised learning algorithm called flocking. Flocking attempts to provide network hyperplane divisions that are evenly influenced by examples on either side of the hyperplane. The second addition is that of a multi-tasking approach called convergence training. Convergence training uses the information provided by a clustering algorithm in order to create subtasks that represent the divisions between clusters. These subtasks are then trained in unison in order to promote hyperplane sharing within the problem space. Generalization was improved in most cases and the solutions produced by the new learning algorithms are demonstrated to be very robust against different random weight initializations. This research is not only a search for better generalizing ANN learning algorithms, but also a search for better understanding when dealing with the complexities involved in ANN generalization.
Author : Ludmila I. Kuncheva
Publisher : John Wiley & Sons
ISBN 13 : 0471660256
Total Pages : 372 pages
Book Rating : 4.4/5 (716 download)
Download or read book Combining Pattern Classifiers written by Ludmila I. Kuncheva and published by John Wiley & Sons. This book was released on 2004-08-20 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.
Author : Osval Antonio Montesinos López
Publisher : Springer Nature
ISBN 13 : 3030890104
Total Pages : 707 pages
Book Rating : 4.0/5 (38 download)
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author : Lei Huang
Publisher : Springer Nature
ISBN 13 : 303114595X
Total Pages : 117 pages
Book Rating : 4.0/5 (311 download)
Download or read book Normalization Techniques in Deep Learning written by Lei Huang and published by Springer Nature. This book was released on 2022-10-08 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.
Author : Adrian Tovar Lopez
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
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.
Author : Huchuan Lu
Publisher : Springer
ISBN 13 : 3030228088
Total Pages : 630 pages
Book Rating : 4.0/5 (32 download)
Download or read book Advances in Neural Networks – ISNN 2019 written by Huchuan Lu and published by Springer. This book was released on 2019-06-26 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.
Author : Bernadete Ribeiro
Publisher : Springer Science & Business Media
ISBN 13 : 3211273891
Total Pages : 561 pages
Book Rating : 4.2/5 (112 download)
Download or read book Adaptive and Natural Computing Algorithms written by Bernadete Ribeiro and published by Springer Science & Business Media. This book was released on 2005-12-12 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific community and is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001) and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in the field. The series has also been of value to young researchers wishing both to extend their knowledge and experience and also to meet internationally renowned experts. The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra in Portugal, drawing on the experience of previous events, and following the same general model, combining technical sessions, including plenary lectures by renowned scientists, with tutorials.
Author : E Fiesler
Publisher : CRC Press
ISBN 13 : 1420050648
Total Pages : 1094 pages
Book Rating : 4.4/5 (2 download)
Download or read book Handbook of Neural Computation written by E Fiesler and published by CRC Press. This book was released on 2020-01-15 with total page 1094 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
