Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Advances in Engineering Software
Simulated minimum Hellinger distance estimation of stochastic volatility models
Computational Statistics & Data Analysis
Classification of toxigenic and atoxigenic strains of Aspergillus flavus with hyperspectral imaging
Computers and Electronics in Agriculture
Computational Statistics & Data Analysis
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
Joint relevance and answer quality learning for question routing in community QA
Proceedings of the 21st ACM international conference on Information and knowledge management
Online learning for fast segmentation of moving objects
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Pattern Recognition
Aggregative quantification for regression
Data Mining and Knowledge Discovery
Hybrid random subsample classifier ensemble for high dimensional data sets
International Journal of Hybrid Intelligent Systems
International Journal of Approximate Reasoning
Hi-index | 35.68 |
The paper algorithmically and empirically studies two major types of nonparametric multivariate density estimation techniques, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is the popular kernel method (and several of its variants) which uses locally tuned radial basis (e.g., Gaussian) functions to interpolate the multidimensional density; the second type is based on an exploratory projection pursuit technique which interprets the multidimensional density through the construction of several 1D densities along highly “interesting” projections of multidimensional data. Performance evaluations using training data from mixture Gaussian and mixture Cauchy densities are presented. The results show that the curse of dimensionality and the sensitivity of control parameters have a much more adverse impact on the kernel density estimators than on the projection pursuit density estimators