Tailoring density estimation via reproducing kernel moment matching
Proceedings of the 25th international conference on Machine learning
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
INDIE: An Artificial Immune Network for On-Line Density Estimation
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A framework for machine learning based on dynamic physical fields
Natural Computing: an international journal
Online Geovisualization with Fast Kernel Density Estimator
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Image Processing
Fast Parzen Window density estimator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Kernel bandwidth estimation for nonparametric modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online kernel density estimation for interactive learning
Image and Vision Computing
Sparse kernel modelling: a unified approach
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Robotics and Computer-Integrated Manufacturing
A framework for visual-context-aware object detection in still images
Computer Vision and Image Understanding
Simplifying mixture models through function approximation
IEEE Transactions on Neural Networks
Computing Color Transforms with Applications to Image Editing
Journal of Mathematical Imaging and Vision
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
Probability density estimation with tunable kernels using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
Multivariate online kernel density estimation with Gaussian kernels
Pattern Recognition
A complete gradient clustering algorithm formed with kernel estimators
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Simple solvers for large quadratic programming tasks
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Probability density estimation based on nonparametric local kernel regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Dynamic data condensation for classification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Feature weighting by RELIEF based on local hyperplane approximation
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Reduced set density estimator for object segmentation based on shape probabilistic representation
Journal of Visual Communication and Image Representation
Computational intelligence in astronomy --- a win-win situation
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
Density estimation with minimization of U-divergence
Machine Learning
Robust kernel density estimation
The Journal of Machine Learning Research
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
White box radial basis function classifiers with component selection for clinical prediction models
Artificial Intelligence in Medicine
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The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a well-known problem. This paper presents the Reduced Set Density Estimator that provides a kernel-based density estimator which employs a small percentage of the available data sample and is optimal in the L2 sense. While only requiring 𝒪(N2) optimization routines to estimate the required kernel weighting coefficients, the proposed method provides similar levels of performance accuracy and sparseness of representation as Support Vector Machine density estimation, which requires 𝒪(N3) optimization routines, and which has previously been shown to consistently outperform Gaussian Mixture Models. It is also demonstrated that the proposed density estimator consistently provides superior density estimates for similar levels of data reduction to that provided by the recently proposed Density-Based Multiscale Data Condensation algorithm and, in addition, has comparable computational scaling. The additional advantage of the proposed method is that no extra free parameters are introduced such as regularization, bin width, or condensation ratios, making this method a very simple and straightforward approach to providing a reduced set density estimator with comparable accuracy to that of the full sample Parzen density estimator.