Path: utzoo!attcan!uunet!decwrl!sdd.hp.com!samsung!xylogics!bu.edu!bucasb!reynolds From: reynolds@bucasd.bu.edu (John Reynolds) Newsgroups: comp.ai.neural-nets Subject: Re: Summary (long): pattern-recognition comparisons Message-ID: Date: 1 Aug 90 16:42:45 GMT References: <23979@boulder.Colorado.EDU> Sender: news@bu.edu.bu.edu Organization: Boston University Center for Adaptive Systems Lines: 41 In-reply-to: fozzard@boulder.Colorado.EDU's message of 26 Jul 90 16:42:52 GMT I suggest you write Sheri Gish IBM Knowledge Based Systems 2800 Sand Hill Road Menlo Park, CA 94025 or W.E. Blanz IBM Research Almaden Research Center 650 Harry Road San Jose, CA 95120 and request their recently (6/19/89) published research report "Comparing a Connectionist Trainable Classifier with Classical Statistical Decision Analysis Methods" (report # RJ 6891 (65717)) Their report critically analyzes the performance of a connectionist (simple back prop) with a Gaussian and three polynomial (linear, quadratic, and cubic) classifiers on a variety of data sets. The results unambiguously support the connectionist system as a viable alternative to the standard techniques, especially for larger problems. In every case its results are comparable to or better than the other methods. The data sets are designed to test the classifiers' success in handling (1) different degrees of separability (2) overlapping distributions (3) outliers (in which case the connectionist is *far* superior to all but the cubic polynomial classifier (i.e. it achieved perfect classification whereas the linear polynomial classifier achieved a 63.3% error rate on both the test set and the training set) and (4) non-informative features. The connectionist system also did better than all but the cubic polynomial solution in a real world image classification task. They also found that while the standard techniques were cheaper for small problems, for problems of realistic size, the connectionist system was superior. -john