285 Introduction to this special issue

Abstract

Three classifiers from machine learning (the generalized linear mixed model, memory based learning, and support vector machines) are compared with a naive discriminative learning classifier, derived from basic principles of errordriven learning characterizing animal and human learning. Tested on the dative alternation in English, using the Switchboard data from (BRESNAN; CUENI; NIKITINA; BAAYEN, 2007), naive discriminative learning emerges with stateof-the-art predictive accuracy. Naive discriminative learning offers a united framework for understanding the learning of probabilistic distributional patterns, for classification, and for a cognitive grounding of distinctive collexeme analysis.

Topics

    15 Figures and Tables

    Download Full PDF Version (Non-Commercial Use)