Exploratory data analysis with artificial immune systems

@article{citeulike:2753148, abstract = {Abstract\ \ We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results from a number of methods, we only report on linear projections which have unique solutions. We incorporate a type of Gram Schmidt orthogonalisation [15] into the affinity maturation process to capture multiple components. We combine the new algorithm with reinforcement learning [17, 20] and with cross entropy maximization [13, 19]. Finally we combine several different non-standard adaptation methods using bagging and show that we get reliable convergence to accurate filters.}, author = {Wu, Ying and Fyfe, Colin }, citeulike-article-id = {2753148}, journal = {Evolutionary Intelligence}, keywords = {application, artificial-immune-system, data-analysis}, posted-at = {2008-05-04 11:09:44}, priority = {2}, title = {Exploratory data analysis with artificial immune systems}, url = {http://dx.doi.org/10.1007/s12065-008-0012-x} }

TY - JOUR ID - citeulike:2753148 L3 - citeulike-article-id:2753148 TI - Exploratory data analysis with artificial immune systems JF - Evolutionary Intelligence N2 - Abstract  We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results from a number of methods, we only report on linear projections which have unique solutions. We incorporate a type of Gram Schmidt orthogonalisation [15] into the affinity maturation process to capture multiple components. We combine the new algorithm with reinforcement learning [17, 20] and with cross entropy maximization [13, 19]. Finally we combine several different non-standard adaptation methods using bagging and show that we get reliable convergence to accurate filters. KW - application KW - artificial-immune-system KW - data-analysis AU - Wu, Ying AU - Fyfe, Colin UR - http://dx.doi.org/10.1007/s12065-008-0012-x ER -