Original Contribution: Stacked generalization
Neural Networks
Machine Learning
GTM: the generative topographic mapping
Neural Computation
Learning in graphical models
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Linearly Combining Density Estimators via Stacking
Machine Learning
Grouping Character Shapes by Means of Genetic Programming
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Nonlinear dimensionality reduction using probabilistic principal surfaces
Nonlinear dimensionality reduction using probabilistic principal surfaces
Probabilistic Principal Surfaces for Yeast Gene Microarray Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
IEEE Transactions on Neural Networks
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Probabilistic Principal Surfaces (PPS) offer very powerful visualization and classification capabilities and overcome most of the shortcomings of other neural tools such as SOM, GTM, etc. More specifically PPS build a probability density function of a given data set of patterns lying in a D-dimensional space (with D ≫ 3) which can be expressed in terms of a limited number of latent variables laying in a Q-dimensional space (Q is usually 2-3) which can be used to visualize the data in the latent space. PPS may also be arranged in ensembles to tackle very complex classification tasks. Competitive Evolution on Data (CED) is instead an evolutionary system in which the possible solutions (cluster centroids) compete to conquer the largest possible number of resources (data) and thus partition the input data set in clusters. We discuss the application of Spherical-PPS to two data sets coming, respectively, from astronomy (Great Observatory Origins Deep Survey) and from genetics (microarray data from yeast genoma) and of CED to the genetics data only.