Announcing a NIPS '90 workshop comparing decision trees and neural nets

[Lorien Y. Pratt]

		Neural Networks and Decision Tree Induction:  
	  Exploring the relationship between two research areas 


      A NIPS '90 workshop, 11/30/1990 or 12/1/1990,  Keystone, Colorado


			    Workshop Co-Chairs: 
		       L. Y. Pratt and S. W. Norton

    The fields of Neural Networks and Machine Learning have evolved
    separately in many ways.  However, close examination of multilayer
    perceptron learning algorithms (such as Back-Propagation) and decision
    tree induction methods (such as ID3 and CART) reveals that there is
    considerable convergence between these subfields.  They address similar
    problem classes (inductive classifier learning) and can be
    characterized by a common representational formalism of hyperplane
    decision regions.  Furthermore, topical subjects within both fields are
    related, from minimal trees and network reduction schemes to
    incremental learning.

    In this workshop, invited speakers from the Neural Network and
    Machine Learning communities will discuss their empirical and
    theoretical comparisons of the two areas, and then present work at
    the interface between these two fields which takes advantage of the
    potential for technology transfer between them.  In a discussion
    period, we'll discuss our conclusions, comparing the methods along
    the dimensions of representation, learning, and performance.  We'll
    debate the ``strong convergence hypothesis'' that these two
    research areas are really studying the same problem.

			  Schedule of talks:
AM: 
7:30-7:50	Lori Pratt	Introductory remarks
7:50-8:10 	Tom Dietterich 	Evidence For and Against Convergence: 
 				Experiments Comparing ID3 and BP 
8:15-8:35 	Les Atlas  	Is backpropagation really better than 
				classification and regression trees? 
8:40-9:00 	Ah Chung Tsoi  	Comparison of the performance of some popular 
				machine learning algorithms: CART, C4.5, and 
				multi-layer perceptrons
9:05-9:25 	Ananth Sankar  	Neural Trees: A Hybrid Approach to Pattern 
				Recognition 

PM: 
4:30-4:55 	Stephen Omohundro  A Bayesian View of Learning with Tree
				Structures and Neural Networks
5:00-5:20 	Paul Utgoff 	Linear Machine Decision Trees 
5:25-5:45 	Terry Sanger 	Basis Function Trees as a Generalization of 
				CART, MARS, and Other Local Variable Selection 
				Techniques 
5:50-6:30 	Discussion, wrap-up  

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     L. Y. Pratt				S. W. Norton
     pratt at paul.rutgers.edu,			norton at learning.siemens.com
     Rutgers University Computer Science Dept.  Siemens Corporate Research
     New Brunswick, NJ  08903.  		755 College Road East
     (201) 932-4634 				Princeton, NJ  08540
						(609) 734-3365