However, the space of possible representations is virtually infinite, so choosing a good representation is not a simple task. This chapter describes a method whereby the selection of a good input representation for classification tasks is automated. Thus, the features included in the trees constitute compact and informative sets which can then be used as input representations for other learning algorithms attacking the same problem. We present here some work using this approach to produce augmentations of artificial neural network input representations for the molecular biology problem of predicting protein secondary structures. We present a parallel approach for integrating speech and natural language understanding.
Processing is carried out by passing multiple markers in parallel through the knowledge base.
Speech-specific problems such as insertion, deletion, substitution, and word boundary detection have been analyzed and their parallel solutions are provided. We have implemented the system on a SparcStation 2, with a translation database consisting of 7000 technical terms in Computer Science. The translation performance of MBT3n improves as the number of processors it runs on increases. MBT3n with 256 processors on an nCUBE2 is about ten times faster than the original MBT3 system on SparcStation2 in the best case. DETE's learning abilities result from a novel neural network architecture, called Katamic memory.