			Universal Problem Solvers, Inc.  Products 
 
 
(1) 	Multi-Pass Instance-Based Learning  
	(Pattern Recognition Software) is also available from: 
 
DEMO available from: 
 
	SimTel/win3/neurlnet/ 
	mpil10.zip      Multi-Pass Instance-Based Learning 
 
Overview 
 
MPIL is an instance-based learning system (instances are simply viewed as  
points in n-dimensional real-space with an associated neighborhood), which  
utilizes two models for creating neighborhoods.  The first model (i.e., MPIL-1)  
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  
(i.e., MPIL-2) 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. 
    
Highlights: 
 
	(1) Allows user to create an abstract instance representation of a training 
	    set.  
	     
	(2) Provides features for Saving and Loading the abstract instance representation. 
	 
	(3) Supports two modes for instance-based learning: MPIL-1 and MPIL-2. 
	 
	(4) Supplies the user with the capability to test and classify new patterns. 
	 
	(5) Allows batch training and testing of a data set (i.e., n-fold crossvalidation) for 
	    a user defined start partition size, end size, delta stepsize and parameter n. 
 
	(6) Contains 21 example data sets. 
 
	(7) Incremental Training 
 
	(8) Can handle up to 2^16 training patterns (depended on available 
	    system memory). 
    
	Latest Version: MPIL v. 1.01 
	Cost: US$20 + US$3 Shipping and Handling. 
	PRICES SUBJECT TO CHANGE WITHOUT NOTIFICATION. 
	Please, enquire for current price list prior to ordering (see address/e-mail below). 
   
 
 
(2)	fSC-Net v. 1.0  
	(Fuzzy Symbolic Connectionist Network) 
 
Overview 
 
	fSC-Net utilizes a hybrid structure in which symbolic as well as connectionist features 
	are exploited.  Learning in fSC-Net only requires a single pass through the training data. 
	In other words the system is a true incremental learner.  New hidden units are automatically 
	recruited, thereby eliminating the need for time consuming parameter tuning.  A pruning 
	mechanism is also available.  Uncertainty management in the form of fuzzy logic has also been  
	incorporated.  Fuzzy pi-shaped membership functions can either be pre-loaded (designed by 
	the knowledge engineer), or can be automatically constructed by fSC-Net.  Finally, rules can be 
	readily extracted and loaded within the system, thereby creating a system capable of supporting 
	knowledge refinement.  Graphical support is also included.  
 
Anticipated Price: 
 
	US$25 + US$3 Shipping and Handling. 
	PRICES SUBJECT TO CHANGE WITHOUT NOTIFICATION. 
	Please, enquire for current price list prior to ordering(see address/e-mail below). 
 
 
Contact: 
 
	Surface mail:	Universal Problem Solvers, Inc. 
			610 South Duncan Avenue 
			Clearwater, FL 34616 
 
	e-mail:		zlxx69a@prodigy.com 
 
	WWW:		http://pages.prodigy.com/FL/lizard/index.html 
 
Mailing List:  
 
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