MultiPass Instance Learning
MPIL is an instance-based learning system , which utilizes two models for creating neighborhoods. The first model places a single neighborhood sphere (based on Euclidean distance measure) around an instance, and is in nature similar to the nearest neighbor classifier, except that it removes redundant instances. The second model incorporates N radii (one for each input of an instance). This model also supports knowledge acquisition in the form of rule extraction. In a sense, both approaches are similar to neural networks in that they exploit a very similar parallelism. MPIL represents a good alternative in cases were large amounts of data have to be learned and provides good facilities for storage reduction. Win32 version.
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MultiPass Instance Learning Review
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