Perceptron Trees: A Case Study in Hybrid Concept Representations

Paul E. Utgoff

The paper presents a case study in examining the bias of two particular formalisms: decision trees and linear threshold units. The immediate result is a new hybrid representation, called a perceptron tree, and an associated learning algorithm called the perceptron tree error correction procedure. The longer term result is a model for exploring issues related to understanding representational bias and constructing other useful hybrid representations.

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