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Parallel fitting of additive models for regression

Khakhutskyy, Valeriy; Hegland, Markus


To solve big data problems which occur in modern data mining applications, a comprehensive approach is required that combines a flexible model and an optimisation algorithm with fast convergence and a potential for efficient parallelisation both in the number of data points and the number of features. In this paper we present an algorithm for fitting additive models based on the basis expansion principle. The classical backfitting algorithm that solves the underlying normal equations cannot be...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Journal article
Source: Lecture Notes in Computer Science
DOI: 10.1007/978-3-319-11206-0_24


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