- author = "Ilknur Icke and Nicholas A. Allgaier and Christopher M. Danforth and Robert A. Whelan and Hugh P. Garavan and Joshua C. Bongard",
- title = "A Deterministic and Symbolic Regression Hybrid Applied to Resting-State {fMRI} Data",
- booktitle = "Genetic Programming Theory and Practice XI",
- year = "2013",
- series = "Genetic and Evolutionary Computation",
- editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek",
- publisher = "Springer",
- chapter = "9",
- pages = "155--173",
- address = "Ann Arbor, USA",
- month = "9-11 " # may,
- keywords = "genetic algorithms, genetic programming, Symbolic regression, Hybrid algorithm, Regularisation, Resting-state fMRI",
- isbn13 = "978-1-4939-0374-0",
- oai = "oai:CiteSeerX.psu:10.1.1.368.634",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.368.634",
-
URL = "
http://www.cs.uvm.edu/~jbongard/papers/2013_GPTP_Icke.pdf",
-
DOI = "
10.1007/978-1-4939-0375-7_9",
- abstract = "Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.",
-
notes = "
Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013",
