- author = "Michael D. Schmidt and Hod Lipson",
- title = "Learning noise",
- booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation",
- year = "2007",
- editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener",
- volume = "2",
- isbn13 = "978-1-59593-697-4",
- pages = "1680--1685",
- address = "London",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1680.pdf",
-
DOI = "
doi:10.1145/1276958.1277289",
- publisher = "ACM Press",
- publisher_address = "New York, NY, USA",
- month = "7-11 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, dynamical systems, stochastic elements, symbolic regression",
- abstract = "In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data by modelling the maximum likelihood output or least squared error, assuming that noise effects average out. While this process works well for data with symmetrical noise distributions (such as Gaussian observation noise), many real-life sources of noise are not symmetrically distributed, thus this approach does not hold. We suggest improved learning can be obtained by including noise sources explicitly in the model as a stochastic element. A stochastic element is a random sub-process or latent variable of a hidden system that can propagate nonlinear noise to the observable outputs. Stochastic elements can skew and distort output features making regression of analytical models particularly difficult and error minimising approaches inhibiting. We introduce a new method to infer the analytical model of a system by decomposing non-uniform noise observed at the outputs into uniform stochastic elements appearing symbolically inside the system. Results demonstrate the ability to regress exact analytical models where stochastic elements are embedded inside nonlinear and polynomial hidden systems.",
-
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071",
