Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
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
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pseudo-density estimation for clustering with gaussian processes
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
Constructing sparse kernel machines using attractors
IEEE Transactions on Neural Networks
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Fast support-based clustering method for large-scale problems
Pattern Recognition
Predicting a distribution of implied volatilities for option pricing
Expert Systems with Applications: An International Journal
Dynamic pattern denoising method using multi-basin system with kernels
Pattern Recognition
Transductive Bayesian regression via manifold learning of prior data structure
Expert Systems with Applications: An International Journal
Forecasting nonnegative option price distributions using Bayesian kernel methods
Expert Systems with Applications: An International Journal
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In this letter, we develop a gaussian process model for clustering. The variances of predictive values in gaussian processes learned from a training data are shown to comprise an estimate of the support of a probability density function. The constructed variance function is then applied to construct a set of contours that enclose the data points, which correspond to cluster boundaries. To perform clustering tasks of the data points, an associated dynamical system is built, and its topological invariant property is investigated. The experimental results show that the proposed method works successfully for clustering problems with arbitrary shapes.