TY - GEN
T1 - Adaptive sparse grid classification using grid environments
AU - Pflüger, Dirk
AU - Muntean, Ioan Lucian
AU - Bungartz, Hans Joachim
PY - 2007
Y1 - 2007
N2 - Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used. This allows for classification algorithms scaling only linearly in the number of training data points, enabling to learn from data sets with millions of data points. As the curse of dimensionality prohibits the use of standard grids, sparse grids have to be used. Adaptive sparse grids allow to get a trade-off between both worlds by refining in rough regions of the target function rather than in smooth ones. We present new results for some typical classification tasks and show first observations of dimension adaptivity. As the study of the critical parameters during development involves many computations for different parameter values, we used a grid environment which we present.
AB - Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used. This allows for classification algorithms scaling only linearly in the number of training data points, enabling to learn from data sets with millions of data points. As the curse of dimensionality prohibits the use of standard grids, sparse grids have to be used. Adaptive sparse grids allow to get a trade-off between both worlds by refining in rough regions of the target function rather than in smooth ones. We present new results for some typical classification tasks and show first observations of dimension adaptivity. As the study of the critical parameters during development involves many computations for different parameter values, we used a grid environment which we present.
KW - Adaptive sparse grids
KW - Classification
KW - Data mining
KW - Grid environment
UR - http://www.scopus.com/inward/record.url?scp=37249074053&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-72584-8_94
DO - 10.1007/978-3-540-72584-8_94
M3 - Conference contribution
AN - SCOPUS:37249074053
SN - 9783540725831
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 708
EP - 715
BT - Computational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
PB - Springer Verlag
T2 - 7th International Conference on Computational Science, ICCS 2007
Y2 - 27 May 2007 through 30 May 2007
ER -