Browsing MIT by Subject "Classification"
Now showing items 1-4 of 4
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Discriminative Gaussian Process Latent Variable Model for Classification
(2007-03-28)Supervised learning is difficult with high dimensional input spacesand very small training sets, but accurate classification may bepossible if the data lie on a low-dimensional manifold. GaussianProcess Latent Variable ...
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Permutation Tests for Classification
(2003-08-28)We introduce and explore an approach to estimating statisticalsignificance of classification accuracy, which is particularly usefulin scientific applications of machine learning where highdimensionality of the data and the ...
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Permutation Tests for Classification
(2003-08-28)We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and ...
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Support Vector Machines: Training and Applications
(1997-03-01)The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique ...