TY - CONF
T1 - Generalisation and Model Selection in Supervised Learning with Evolutionary Computation
AU - Rowland, Jeremy John
N1 - Rowland, J. J. (2003) Generalisation and Model Selection in Supervised Learning with Evolutionary Computation. European Workshop on Evolutionary Computation in Bioinformatics: EvoBio 2003. Lecture Notes in Computer Science (Springer), Vol 2611, pp 119-130
PY - 2003
Y1 - 2003
N2 - EC-based supervised learning has been demonstrated to be an effective approach to forming predictive models in genomics, spectral interpretation, and other problems in modern biology. Longer-established methods such as PLS and ANN are also often successful. In supervised learning, overtraining is always a potential problem. The literature reports numerous methods of validating predictive models in order to avoid overtraining. Some of these approaches can be applied to EC-based methods of supervised learning, though the characteristics of EC learning are different from those obtained with PLS and ANN and selecting a suitably general model can be more difficult. This paper reviews the issues and various approaches, illustrating salient points with examples taken from applications in bioinformatics.
AB - EC-based supervised learning has been demonstrated to be an effective approach to forming predictive models in genomics, spectral interpretation, and other problems in modern biology. Longer-established methods such as PLS and ANN are also often successful. In supervised learning, overtraining is always a potential problem. The literature reports numerous methods of validating predictive models in order to avoid overtraining. Some of these approaches can be applied to EC-based methods of supervised learning, though the characteristics of EC learning are different from those obtained with PLS and ANN and selecting a suitably general model can be more difficult. This paper reviews the issues and various approaches, illustrating salient points with examples taken from applications in bioinformatics.
UR - http://www.scopus.com/inward/record.url?scp=35248824577&partnerID=8YFLogxK
U2 - 10.1007/3-540-36605-9_12
DO - 10.1007/3-540-36605-9_12
M3 - Paper
SP - 119
EP - 130
ER -