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Title:How to keep the HG weights non-negative: the truncated Perceptron reweighing rule
Authors: Giorgio Magri
Comment:to appear in the Journal of Language Modelling
Abstract:The literature on error-driven learning in Harmonic Grammar (HG) has adopted the Perceptron reweighing rule. Yet, this rule is not suited to HG, as it fails at ensuring non-negative weights. A variant is thus considered which truncates the updates at zero, keeping the weights non-negative. Convergence guarantees and error bounds for the original Perceptron are shown to extend to its truncated variant.
Type:Paper/tech report
Area/Keywords:learnability, error-driven learning, HG, Perceptron
Article:Version 1