Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminative Gaussian process latent variable model for classification
Proceedings of the 24th international conference on Machine learning
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It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, we "learn" the location of the data. This way we (i) do not need a metric (or even stronger structure) - pairwise dissimilarities suffice; and additionally (ii) produce low-dimensional embeddings that can be analyzed visually. We achieve this by combining an entropy-based embedding method with an entropy-based version of semi-supervised logistic regression. We present results for clustering and semi-supervised classification.