Classifier systems
The SOM idea didn't necessarily fail. In fact the pyramid system that I'm using instead is similar to a SOM in that input images are classified accoring to their euclidean distance from a set of stored examples. The main difference is that where the dimensions of the traditional SOM remain fixed the pyramid system expands to conver the feature space.
In my training example the pyramid system eventually reached a feature space size of around 700 exemplars, whereas the SOM that I was using before only used 10x10 = 100 possible features. To some extent this explains why the SOM never reached any stable classifications, but even if the map dimensions had been larger this would not have got around the problem of classifications shifting around unpredictably over the two dimensional surface of the SOM. The pyramid system is also slightly more unconventional in that it uses a recursive chess-like search through the feature space.
As for the incremental learning system that you describe it would be possible to do this provided that the speech recognition was accurate, or at least if there were an accurate way of recording speech waveforms for analysis. This is only usually possible with current technology if the microphone is very close to your mouth. A while ago I hunted around for the ideal speech recognition gadget - a radio mic - but couldn't find anyone selling them.
The main problem with the pyramid classifier at the moment is that like an elephant it never forgets anything, so that the feature space just keeps on increasing depending on the robots experience. I'll need to include some system whereby exemplars which are infrequently used, or which are not associated with important events, are selectively removed over time.
The amount of time taken to code the SOM was almost zero, since I wrote it more than a year ago and it's on my web site. The pyramid system too most of Sunday morning to code.
- Bob
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