NDLI: An Optimal Regression Algorithm for Piecewise Functions Expressed as Object-Oriented Programs

Content Provider	IEEE Xplore Digital Library
Author	Luo, J. Brodsky, A.
Copyright Year	2010
Abstract	Core Java is a framework which extends the programming language Java with built-in regression analysis, i.e., the capability to do parameter estimation for a function. Core Java is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of Core Java include calibration of parameters of computational processes, described as OO programs. If-then-else statements of Java language are naturally adopted to create piecewise functional forms of regression. Thus, minimization of the sum of least squared errors involves an optimization problem with a search space that is exponential to the size of learning set. In this paper, we propose a combinatorial restructuring algorithm which guarantees learning optimality and furthermore reduces the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds.
Starting Page	937
Ending Page	942
File Size	509500
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Java Linear regression Piecewise Regression Search problems Combinatorial Restructuring Complexity theory Object-Oriented Programming Spline Optimization
Content Type	Text
Resource Type	Article

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