Parsimony doesn't mean simplicity: Genetic Programming for inductive inference on noisy data

被引：0

作者：

De Falco, Ivanoe ^{[1
]}

Della Cioppa, Antonio ^{[2
]}

Maisto, Domenico ^{[3
]}

Scafuri, Umberto ^{[1
]}

Tarantino, Ernesto ^{[1
]}

机构：

[1] CNR, Inst High Performance Comp & Networking, ICAR CNR, Via P Castellino 111, I-80131 Naples, Italy

[2] Univ Salerno, DIIIE, Natural Comp Lab, I-84084 Salerno, Italy

[3] Univ Modena & Reggio Emilia, I-41100 Modena, Italy

来源：

GENETIC PROGRAMMING, PROCEEDINGS | 2007年 / 4445卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Genetic Programming algorithm based on Solomonoff's probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the algorithm is compared against a classical parsimony-based GP. The results shows the superiority of the Solomonoff-based approach.

引用

页码：351 / +

页数：3

共 13 条

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