Combining Artificial Neural Nets

Ensemble and Modular Multi-Net Systems

AvAmanda J.C. Sharkey

Häftad, Engelska, 1999

Del i serien Perspectives in Neural Computing

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Beskrivning

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.

Produktinformation

Utgivningsdatum:1999-01-22
Mått:155 x 235 x 18 mm
Vikt:482 g
Format:Häftad
Språk:Engelska
Serie:Perspectives in Neural Computing
Antal sidor:298
Upplaga:99001
Förlag:Springer London Ltd
ISBN:9781852330040

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

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.