GTM: the generative topographic mapping
Neural Computation
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Fast Parzen Window density estimator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ECML'06 Proceedings of the 17th European conference on Machine Learning
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation
IEEE Transactions on Neural Networks
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Large archives of astronomical data (images, spectra and catalogues of derived parameters) are being assembled worldwide as part of the Virtual Observatory project. In order for such massive heterogeneous data collections to be of use to astronomers, development of Computational Intelligence techniques that would combine modern machine learning with deep domain knowledge is crucial. Both fields - Computer Science and Astronomy - can hugely benefit from such a research program. Astronomers can gain new insights into structures buried deeply in the data collections that would, without the help of Computational Intelligence, stay masked. On the other hand, computer scientists can get inspiration and motivation for development of new techniques driven by the specific characteristics of astronomical data and the need to include domain knowledge in a fundamental way. In this review we present three diverse examples of such successful symbiosis.