Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trajectory-Based support vector multicategory classifier
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Clustering Based on Gaussian Processes
Neural Computation
One-class classification with gaussian processes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
One-class classification with Gaussian processes
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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Gaussian processes (GP) provide a kernel machine framework. They have been mainly applied to regression and classification. We propose a pseudo-density estimation method based on the information of variance functions of GPs, which relates to the density of the data points. We also show how the constructed pseudo-density can be applied to clustering. Through simulation we show that the topology of the pseudo-density represents the clustering information well with promising results.