Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems by Aman

Description: Combining Artificial Neural Nets by Amanda J.C. Sharkey Similarly the principles of modularity, and of reliability through redundancy, can be found in many disparate areas, from the idea of decision by jury, through to hardware re dundancy in aeroplanes, and the advantages of modular design and reuse advocated by object-oriented programmers. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description The chapters in this volume consist of articles written by leading researchers in the field of Combining Artificial Neural Nets, and as such provide a unique coverage of the area. The techniques that are presented include ensemble-based, and modular approaches. The presentation of techniques is accompanied by analysis and evaluation of their relative effectiveness, and by reports of ther application to a variety of problems. The chapters make clear the effectiveness of combining in increasing performance, and the limitations of using a simple unitary net. For this reason this volume should be required reading for all those concerned with any applications of artificial neural nets where good generalisation performance is important. Notes Springer Book Archives Author Biography Sharkey, University of Sheffield, UK. Table of Contents 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. Promotional Springer Book Archives Long Description The past decade could be seen as the heyday of neurocomputing: in which the capabilities of monolithic nets have been well explored and exploited. The question then is where do we go from here? A logical next step is to examine the potential offered by combinations of artificial neural nets, and it is that step that the chapters in this volume represent. Intuitively, it makes sense to look at combining ANNs. Clearly complex biological systems and brains rely on modularity. Similarly the principles of modularity, and of reliability through redundancy, can be found in many disparate areas, from the idea of decision by jury, through to hardware re Feature 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 Details ISBN185233004X Publisher Springer London Ltd Series Perspectives in Neural Computing Year 1999 ISBN-10 185233004X ISBN-13 9781852330040 Format Paperback Imprint Springer London Ltd Place of Publication England Country of Publication United Kingdom Edited by Amanda J.C. Sharkey DEWEY 006.32 Publication Date 1999-01-22 Short Title COMBINING ARTIFICIAL NEURAL NE Language English Media Book Edition 99001st Pages 298 Subtitle Ensemble and Modular Multi-Net Systems Illustrations 6 Illustrations, black and white; XV, 298 p. 6 illus. DOI 10.1604/9781852330040;10.1007/978-1-4471-0793-4 AU Release Date 1999-01-22 NZ Release Date 1999-01-22 UK Release Date 1999-01-22 Author Amanda J.C. Sharkey Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96324426;

Price: 205.7 AUD

Location: Melbourne

End Time: 2024-12-07T03:09:11.000Z

Shipping Cost: 9.9 AUD