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ICML
2008
IEEE
14 years 6 months ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse...
EDBT
2000
ACM
13 years 9 months ago
Dynamically Optimizing High-Dimensional Index Structures
In high-dimensional query processing, the optimization of the logical page-size of index structures is an important research issue. Even very simple query processing techniques suc...
Christian Böhm, Hans-Peter Kriegel
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
13 years 11 months ago
Enhancing differential evolution performance with local search for high dimensional function optimization
In this paper, we proposed Fittest Individual Refinement (FIR), a crossover based local search method for Differential Evolution (DE). The FIR scheme accelerates DE by enhancing...
Nasimul Noman, Hitoshi Iba
CSB
2003
IEEE
150views Bioinformatics» more  CSB 2003»
13 years 10 months ago
Algorithms for Bounded-Error Correlation of High Dimensional Data in Microarray Experiments
The problem of clustering continuous valued data has been well studied in literature. Its application to microarray analysis relies on such algorithms as -means, dimensionality re...
Mehmet Koyutürk, Ananth Grama, Wojciech Szpan...
ICCS
2001
Springer
13 years 10 months ago
Parallel High-Dimensional Integration: Quasi-Monte Carlo versus Adaptive Cubature Rules
Abstract Parallel algorithms for the approximation of a multi-dimensional integral over an hyper-rectangular region are discussed. Algorithms based on quasi-Monte Carlo techniques ...
Rudolf Schürer