Combining Artificial Neural Nets (eBook, PDF)

Ensemble and Modular Multi-Net Systems
Redaktion: Sharkey, Amanda J. C.

• Format: PDF

Produktbeschreibung

This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their relative effectiveness and their application to a variety of problems.

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Produktdetails

• Verlag: Springer London
• Seitenzahl: 298
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781447107934
• Artikelnr.: 44000776

Inhaltsangabe

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.