Combining Artificial Neural Nets Ensemble and Modular Multi-Net Systems [Paperback]

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.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? dundancy in aeroplanes, and the advantages of modular design and reuse advocated by object-oriented programmers. And it is not surprising to find that the same principles can be usefully applied in the field of neurocomput? ing as well, although finding the best way of adapting them is a subject of on-going research.1. Multi-Net Systems.- 1.0.1 Different Forms of Multi-Net System.- 1.1 Ensembles.- 1.1.1 Why Create Ensembles?.- 1.1.2 Methods for Creating Ensemble Members.- 1.1.3 Methods for Combining Nets in Ensembles.- 1.1.4 Choosing a Method for Ensemble Creation and Combination.- 1.2 Modular Approaches.- 1.2.1 Why Create Modular Systems?.- 1.2.2 Methods for Creating Modular Components.- 1.2.3 Methods for Combining Modular Components.- 1.3 The Chapters in this Book.- 1.4 References.- 2. Combining Predictors.- 2.1 Combine and Conquer.- 2.2 Regression.- 2.2.1 Bias alóQ