Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences

We investigate a form of modular neural network for classification with (a) pre-separated input vectors entering its specialist (expert) networks, (b) specialist networks which are selforganized (radial-basis function or self-targeted feedforward type) and (c) which fuses (or integrates) the specialists with a single-layer net. When the modular architecture is applied to spatiotemporal sequences, the Specialist Nets are recurrent; specifically, we use the Input Recurrent type. The Specialist Networks (SNs) learn to divide their input space into a number of equivalence classes defined by self-organized clustering and learning using the statistical properties of the input domain. Once the specialists have settled in their training, the Fusion Network is trained by any supervised method to map to the semantic classes. We discuss the fact that this architecture and its training is quite distinct from the hierarchical mixture of experts (HME) type as well as from stacked generalization. Because the equivalence classes to which the SNs map the input vectors are determined by the natural clustering of the input data, the SNs learn rapidly and accurately. The fusion network also trains rapidly by reason of its simplicity. We argue, on theoretical grounds, that the accuracy of the system should be positively correlated to the product of the number of equivalence classes for all of the SNs. This network was applied, as an empirical test case, to the classification of melodies presented as direct audio events (temporal sequences) played by a human and subject, therefore, to biological variations. The audio input was divided into two modes: (a) frequency (or pitch) variation and (b) rhythm, both as functions of time. The results and observations show the technique to be very robust and support the theoretical deductions concerning accuracy.