pratt@paul.rutgers.edu (Lorien Y. Pratt) (11/13/90)
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 ------------------------------------------------------------------------------ L. Y. Pratt S. W. Norton pratt@paul.rutgers.edu, norton@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 -- ------------------------------------------------------------------- L. Y. Pratt Computer Science Department pratt@paul.rutgers.edu Rutgers University Hill Center (201) 932-4634 (Hill Center office) New Brunswick, NJ 08903, USA (201) 846-4766 (home)