Sequence join classifier

As with all the studies which directly use perceptual evidence, the amount of data available for training is often very limited due to the high cost of collection. It is possible however to consider classifiers which do not rely on making human judgments. This sequence join classifier has a similar philosophy to the probabilistic sequence join function. We use the fact that our entire speech corpus is composed of large amounts of naturally occurring perfect joins , as every single example of two frames found side by side is an example of such. Therefore we have three situations ... 

The purpose of the join function is to tell us how well two units will join together when concatenated. In most approaches this function returns a cost, such that we usually talk about join costs. Other formulations are however possible, including the join classifier, which returns true or false, and the join probability, which returns the probability that two units will be found in sequence. 

As a full-scale family classification system, more than 1200 MOTIFIND neural networks were implemented, one for each ProSite protein group. The training set for the neural networks consisted of both positive (ProSite family members) and negative (randomly selected non-members) sequences at a ratio of 1 to 2. ProClass groups non-redundant SwissProt and PIR protein sequence entries into families as defined collectively by PIR superfamilies and ProSite patterns. By joining global and motif similarities in a single classification scheme, ProClass helps to reveal domain and family relationships, and classify multi-domained proteins. 

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