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A Generalized View On Learning In Feedforward Neural Networks
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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:
Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :1447137485 Total Pages :498 pages Book Rating :4.4/5 (471 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Book Synopsis Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing by : Kamila, Narendra Kumar
Download or read book Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing written by Kamila, Narendra Kumar and published by IGI Global. This book was released on 2015-11-30 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: ###############################################################################################################################################################################################################################################################
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 Encyclopedia of Artificial Intelligence by : Juan Ramon Rabunal
Download or read book Encyclopedia of Artificial Intelligence written by Juan Ramon Rabunal and published by IGI Global. This book was released on 2009-01-01 with total page 1640 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.
Author :Management Association, Information Resources Publisher :IGI Global ISBN 13 :152251760X Total Pages :3095 pages Book Rating :4.5/5 (225 download)
Book Synopsis Artificial Intelligence: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources
Download or read book Artificial Intelligence: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2016-12-12 with total page 3095 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.
Book Synopsis The Theory of Perfect Learning by : Nonvikan Karl-Augustt Alahassa
Download or read book The Theory of Perfect Learning written by Nonvikan Karl-Augustt Alahassa and published by Nonvikan Karl-Augustt Alahassa. This book was released on 2021-08-17 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: The perfect learning exists. We mean a learning model that can be generalized, and moreover, that can always fit perfectly the test data, as well as the training data. We have performed in this thesis many experiments that validate this concept in many ways. The tools are given through the chapters that contain our developments. The classical Multilayer Feedforward model has been re-considered and a novel $N_k$-architecture is proposed to fit any multivariate regression task. This model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don't overfit. His hyper-parameters, the learning rate, the batch size, the number of training times (epochs), the size of each layer, the number of hidden layers, all can be chosen experimentally with cross-validation methods. There is a great advantage to build a more powerful model using mixture models properties. They can self-classify many high dimensional data in a few numbers of mixture components. This is also the case of the Shallow Gibbs Network model that we built as a Random Gibbs Network Forest to reach the performance of the Multilayer feedforward Neural Network in a few numbers of parameters, and fewer backpropagation iterations. To make it happens, we propose a novel optimization framework for our Bayesian Shallow Network, called the {Double Backpropagation Scheme} (DBS) that can also fit perfectly the data with appropriate learning rate, and which is convergent and universally applicable to any Bayesian neural network problem. The contribution of this model is broad. First, it integrates all the advantages of the Potts Model, which is a very rich random partitions model, that we have also modified to propose its Complete Shrinkage version using agglomerative clustering techniques. The model takes also an advantage of Gibbs Fields for its weights precision matrix structure, mainly through Markov Random Fields, and even has five (5) variants structures at the end: the Full-Gibbs, the Sparse-Gibbs, the Between layer Sparse Gibbs which is the B-Sparse Gibbs in a short, the Compound Symmetry Gibbs (CS-Gibbs in short), and the Sparse Compound Symmetry Gibbs (Sparse-CS-Gibbs) model. The Full-Gibbs is mainly to remind fully-connected models, and the other structures are useful to show how the model can be reduced in terms of complexity with sparsity and parsimony. All those models have been experimented, and the results arouse interest in those structures, in a sense that different structures help to reach different results in terms of Mean Squared Error (MSE) and Relative Root Mean Squared Error (RRMSE). For the Shallow Gibbs Network model, we have found the perfect learning framework : it is the $(l_1, \boldsymbol{\zeta}, \epsilon_{dbs})-\textbf{DBS}$ configuration, which is a combination of the \emph{Universal Approximation Theorem}, and the DBS optimization, coupled with the (\emph{dist})-Nearest Neighbor-(h)-Taylor Series-Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model [which in turn is a combination of the research of the Nearest Neighborhood for a good Train-Test association, the Taylor Approximation Theorem, and finally the Multivariate Interpolation Method]. It indicates that, with an appropriate number $l_1$ of neurons on the hidden layer, an optimal number $\zeta$ of DBS updates, an optimal DBS learnnig rate $\epsilon_{dbs}$, an optimal distance \emph{dist}$_{opt}$ in the research of the nearest neighbor in the training dataset for each test data $x_i^{\mbox{test}}$, an optimal order $h_{opt}$ of the Taylor approximation for the Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model once the {\bfseries DBS} has overfitted the training dataset, the train and the test error converge to zero (0). As the Potts Models and many random Partitions are based on a similarity measure, we open the door to find \emph{sufficient} invariants descriptors in any recognition problem for complex objects such as image; using \emph{metric} learning and invariance descriptor tools, to always reach 100\% accuracy. This is also possible with invariant networks that are also universal approximators. Our work closes the gap between the theory and the practice in artificial intelligence, in a sense that it confirms that it is possible to learn with very small error allowed.
Book Synopsis Feed-Forward Neural Networks by : Anne-Johan Annema
Download or read book Feed-Forward Neural Networks written by Anne-Johan Annema and published by Springer Science & Business Media. This book was released on 1995-05-31 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained. Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips. Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation. Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.
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 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 Mathematical Perspectives on Neural Networks by : Paul Smolensky
Download or read book Mathematical Perspectives on Neural Networks written by Paul Smolensky and published by Psychology Press. This book was released on 2013-05-13 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.
Book Synopsis MICAI 2002: Advances in Artificial Intelligence by : Carlos Coello Coello
Download or read book MICAI 2002: Advances in Artificial Intelligence written by Carlos Coello Coello and published by Springer Science & Business Media. This book was released on 2002-03-27 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mérida, Yucatán, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
Book Synopsis Multilayer Neural Networks by : Maciej Krawczak
Download or read book Multilayer Neural Networks written by Maciej Krawczak and published by Springer. This book was released on 2013-04-17 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks. Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book. The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogenous objects, but also for modelling non-homogenous systems, for example systems which consist of a different kind of subsystems. The use of the generalized nets methodology shows a new way to describe functioning of discrete dynamic systems.
Download or read book Neural Networks written by Raul Rojas and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.
Book Synopsis Advances in Swarm Intelligence by : Ying Tan
Download or read book Advances in Swarm Intelligence written by Ying Tan and published by Springer. This book was released on 2019-07-18 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set of LNCS 11655 and 11656 constitutes the proceedings of the 10th International Conference on Advances in Swarm Intelligence, ICSI 2019, held in Chiang Mai, Thailand, in June 2019. The total of 82 papers presented in these volumes was carefully reviewed and selected from 179 submissions. The papers were organized in topical sections as follows: Part I: Novel methods and algorithms for optimization; particle swarm optimization; ant colony optimization; fireworks algorithms and brain storm optimization; swarm intelligence algorithms and improvements; genetic algorithm and differential evolution; swarm robotics. Part II: Multi-agent system; multi-objective optimization; neural networks; machine learning; identification and recognition; social computing and knowledge graph; service quality and energy management.
Book Synopsis Algorithmic Learning Theory by : Nicolò Cesa-Bianchi
Download or read book Algorithmic Learning Theory written by Nicolò Cesa-Bianchi and published by Springer. This book was released on 2003-08-03 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers presented at the 13th Annual Conference on Algorithmic Learning Theory (ALT 2002), which was held in Lub ̈ eck (Germany) during November 24–26, 2002. The main objective of the conference was to p- vide an interdisciplinary forum discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was colocated with the Fifth International Conference on Discovery Science (DS 2002). The volume includes 26 technical contributions which were selected by the program committee from 49 submissions. It also contains the ALT 2002 invited talks presented by Susumu Hayashi (Kobe University, Japan) on “Mathematics Based on Learning”, by John Shawe-Taylor (Royal Holloway University of L- don, UK) on “On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum”, and by Ian H. Witten (University of Waikato, New Zealand) on “Learning Structure from Sequences, with Applications in a Digital Library” (joint invited talk with DS 2002). Furthermore, this volume - cludes abstracts of the invited talks for DS 2002 presented by Gerhard Widmer (Austrian Research Institute for Arti?cial Intelligence, Vienna) on “In Search of the Horowitz Factor: Interim Report on a Musical Discovery Project” and by Rudolf Kruse (University of Magdeburg, Germany) on “Data Mining with Graphical Models”. The complete versions of these papers are published in the DS 2002 proceedings (Lecture Notes in Arti?cial Intelligence, Vol. 2534). ALT has been awarding the E.
Book Synopsis Neural Nets Wirn Vietri '95 - Proceedings Of The Vii Italian Workshop by : Maria Marinaro
Download or read book Neural Nets Wirn Vietri '95 - Proceedings Of The Vii Italian Workshop written by Maria Marinaro and published by World Scientific. This book was released on 1996-01-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the seventh Italian Workshop on Neural Nets WIRN VIETRI '95, organized by the International Institute for Advanced Scientific Studies 'E R Caianiello' (IIASS) and Società Italiana Reti Neuroniche (SIREN).The spectrum of contributors and participants covers the activity of Italian research in the field. The papers of the two invited speakers, M J Jordan ('Sigmoid Belief Networks') and E Oja ('Principal and Independent Component Analysis'), and the two reviews ('Fast Learning Algorithms for Feedforward NN' and 'ANN Ensembles: a Bayesian Standpoint') complete the highly qualified contents of the volume.