2007
NeurIPS
NeurIPS 2007
An Analysis of Inference with the Universum
Abstract
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a pro- jected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.
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Hot Topic Early Bird
- dimensionality reduction
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Core Methods > Kernel Methods
Machine Learning > Core Methods > Support Vector Machine