title: Generalization ability of fractional polynomial models.
abstract: In this paper, the problem of learning the functional dependency between input and output variables from scattered data using fractional polynomial models (FPM) is investigated. The estimation error bounds are obtained by calculating the pseudo-dimension of FPM, which is shown to be equal to that of sparse polynomial models (SPM). A linear decay of the approximation error is obtained for a class of target functions which are dense in the space of continuous functions. We derive a structural risk analogous to the Schwartz Criterion and demonstrate theoretically that the model minimizing this structural risk can achieve a favorable balance between estimation and approximation errors. An empirical model selection comparison is also performed to justify the usage of this structural risk in selecting the optimal complexity index from the data. We show that the construction of FPM can be efficiently addressed by the variable projection method. Furthermore, our empirical study implies that FPM could attain better generalization performance when compared with SPM and cubic splines.
authors.affiliation: State Key Lab of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China. Electronic address: ywlei@whu.edu.cn.
authors.forename: Yunwen
authors.initials: Y
authors.lastname: Lei
authors.forename: Lixin
authors.initials: L
authors.lastname: Ding
authors.forename: Yiming
authors.initials: Y
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005793:Generalization, Psychological; D016018:Least-Squares Analysis; D008959:Models, Neurological; D015233:Models, Statistical
keywords: Approximation theory; Fractional polynomial; Learning algorithm; Learning theory; Model selection