A Framework for the Cooperation of Learning Algorithms
Léon Bottou, Patrick Gallinari · 1990
We introduce a framework for training architectures composed of several modules. This framework, which uses a statistical formulation of learning systems, provides a unique formalism for describing many classical connectionist algorithms as well as complex systems where several algorithms interact. It allows to design hybrid systems which combine the advantages of connectionist algorithms as well as other learning algorithms. 1 INTRODUCTION Many recent achievements in the connectionist area have been carried out by designing systems where different algorithms interact. For example (Bourlard & Morgan, 1991) have mixed a Multi-Layer Perceptron (MLP) with a Dynamic Programming algorithm. Another impressive application (Le Cun, Boser & al., 1990) uses a very complex multilayer architecture, followed by some statistical decision process. Also, in speech or image recognition systems, input signals are sequentially processed through different modules. Modular systems are the most promising wa...