Modelling data: Analogies in neural networks,simulated annealing and genetic algorithms Export

@incollection{citeulike:3885771, abstract = {This paper examines several analogies employed in computational data analysis techniques: the analogy to the brain for artificial neural networks, the analogy to statistical mechanics for simulated annealing and the analogy to evolution for genetic algorithms. After exploring these analogies, we compare them to analogies in scientific models and highlight that scientific models address specific empirical phenomena, whereas data analysis models are application-neutral: they can be used whenever a set of data meets certain formal requirements, regardless of what phenomenon these data pertain to. Through the analogy, computational data analysis techniques inherit a conceptual idea from which the principle of the technique is developed. In all cases of computational data analysis techniques, the analogies used – and the metaphors generated by them – help us to understand the technique by providing a more concrete framework for understanding what is otherwise an abstract method. In the different examples, however, the significance of the analogies varies. Analogy can, though need not, be indispensable for a technique.}, address = {New York}, author = {Bailers-Jones, Daniela M. and Bailers-Jones, Coryn A. L.}, booktitle = {Model-Based Reasoning: Science, Technology, Values}, citeulike-article-id = {3885771}, editor = {Magnani, L. and Nersessian, N.}, keywords = {computation, genetic\_algorithm, neural\_networks, simulated\_annealing}, posted-at = {2009-01-14 16:45:58}, priority = {2}, publisher = {Kluwer Academic/Plenum Publishers}, title = {Modelling data: Analogies in neural networks,simulated annealing and genetic algorithms}, year = {2002} }

TY - CHAP ID - citeulike:3885771 L3 - citeulike-article-id:3885771 N2 - This paper examines several analogies employed in computational data analysis techniques: the analogy to the brain for artificial neural networks, the analogy to statistical mechanics for simulated annealing and the analogy to evolution for genetic algorithms. After exploring these analogies, we compare them to analogies in scientific models and highlight that scientific models address specific empirical phenomena, whereas data analysis models are application-neutral: they can be used whenever a set of data meets certain formal requirements, regardless of what phenomenon these data pertain to. Through the analogy, computational data analysis techniques inherit a conceptual idea from which the principle of the technique is developed. In all cases of computational data analysis techniques, the analogies used – and the metaphors generated by them – help us to understand the technique by providing a more concrete framework for understanding what is otherwise an abstract method. In the different examples, however, the significance of the analogies varies. Analogy can, though need not, be indispensable for a technique. AD - New York TI - Modelling data: Analogies in neural networks,simulated annealing and genetic algorithms PB - Kluwer Academic/Plenum Publishers T2 - Model-Based Reasoning: Science, Technology, Values KW - computation KW - genetic_algorithm KW - neural_networks KW - simulated_annealing AU - Bailers-Jones, Daniela AU - Bailers-Jones, Coryn A2 - Magnani, L A2 - Nersessian, N PY - 2002/// ER -