Combining Artificial Neural Nets - new book

EAN (ISBN-13): 9781852330040
ISBN (ISBN-10): 185233004X
Paperback
Publishing year: 1999
Publisher: Springer

Book in our database since 2008-01-23T20:00:10-05:00 (New York)
Detail page last modified on 2023-09-11T21:54:59-04:00 (New York)
ISBN/EAN: 185233004X

ISBN - alternate spelling:
1-85233-004-X, 978-1-85233-004-0
Alternate spelling and related search-keywords:
Book title: under the net, modular, ensemble, artificial

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Information from Publisher

Author: Amanda J.C. Sharkey
Title: Perspectives in Neural Computing; Combining Artificial Neural Nets - Ensemble and Modular Multi-Net Systems
Publisher: Springer; Springer London
298 Pages
Publishing year: 1999-01-22
London; GB
Language: English
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XV, 298 p. 6 illus.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Ensembl; cognition; genetic algorithm; neural network; proving; simulation; speech recognition; Artificial Intelligence; Computational and Systems Biology; Complex Systems; Theoretical, Mathematical and Computational Physics; DV-gestützte Biologie/Bioinformatik; Kybernetik und Systemtheorie; Mathematische Physik; EA

1. Multi-Net Systems.- 1.0.1 Different Forms of Multi-Net System.- 1.1 Ensembles.- 1.2 Modular Approaches.- 1.3 The Chapters in this Book.- 1.4 References.- 2. Combining Predictors.- 2.1 Combine and Conquer.- 2.2 Regression.- 2.3 Classification.- 2.4 Remarks.- 2.5 Adaboost and Arcing.- 2.6 Recent Research.- 2.7 Coda.- 2.8 References.- 3. Boosting Using Neural Networks.- 3.1 Introduction.- 3.2 Bagging.- 3.3 Boosting.- 3.4 Other Ensemble Techniques.- 3.5 Neural Networks.- 3.6 Trees.- 3.7 Trees vs. Neural Nets.- 3.8 Experiments.- 3.9 Conclusions.- 3.10 References.- 4. A Genetic Algorithm Approach for Creating Neural Network Ensembles.- 4.1 Introduction.- 4.2 Neural Network Ensembles.- 4.3 The ADDEMUP Algorithm.- 4.4 Experimental Study.- 4.5 Discussion and Future Work.- 4.6 Additional Related Work.- 4.7 Conclusions.- 4.8 References.- 5. Treating Harmful Collinearity in Neural Network Ensembles.- 5.1 Introduction.- 5.2 Overview of Optimal Linear Combinations (OLC) of Neural Networks.- 5.3 Effects of Collinearity on Combining Neural Networks.- 5.4 Improving the Generalisation of NN Ensembles by Treating Harmful Collinearity.- 5.5 Experimental Results.- 5.6 Concluding Remarks.- 5.7 References.- 6. Linear and Order Statistics Combiners for Pattern Classification.- 6.1 Introduction.- 6.2 Class Boundary Analysis and Error Regions.- 6.3 Linear Combining.- 6.4 Order Statistics.- 6.5 Correlated Classifier Combining.- 6.6 Experimental Combining Results.- 6.7 Discussion.- 6.8 References.- 7. Variance Reduction via Noise and Bias Constraints.- 7.1 Introduction.- 7.2 Theoretical Considerations.- 7.3 The BootstrapEnsemble with Noise Algorithm.- 7.4 Results on the Two—Spirals Problem.- 7.5 Discussion.- 7.6 References.- 8. A Comparison of Visual Cue Combination Models.- 8.1Introduction.- 8.2 Stimulus.- 8.3 Tasks.- 8.4 Models of Cue Combination.- 8.5 Simulation Results.- 8.6 Summary.- 8.7 References.- 9. Model Selection of Combined Neural Nets for Speech Recognition.- 9.1 Introduction.- 9.2 The Acoustic Mapping.- 9.3 Network Architectures.- 9.4 Experimental Environment.- 9.5 Bootstrap Estimates and Model Selection.- 9.6 Normalisation Results.- 9.7 Continuous Digit Recognition Over the Telephone Network.- 9.8 Conclusions.- 9.9 References.- 10. Self-Organised Modular Neural Networks for Encoding Data.- 10.1 Introduction.- 10.2 Basic Theoretical Framework.- 10.3 Circular Manifold.- 10.4 Toroidal Manifold: Factorial Encoding.- 10.5 Asymptotic Results.- 10.6 Approximate the Posterior Probability.- 10.7 Joint Versus Factorial Encoding.- 10.8 Conclusions.- 10.9 References.- 11. Mixtures of X.- 11.1 Introduction.- 11.2 Mixtures of X.- 11.3 Summary.- 11.4 References.
There are no other books covering both modular and ensemble approaches (The ensemble approach uses a variety of methods to create a set of different nets trained on the same task; the modular approach decomposes a task into simpler problems) The presentation of techniques is accompanied by analysis and evaluation of their relative effectiveness on a variety of problems The book focuses on the combination of neural nets, but many of the methods are applicable to a wider variety of statistical methods