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NIPS
1994

Recognizing Handwritten Digits Using Mixtures of Linear Models

8 years 11 months ago
Recognizing Handwritten Digits Using Mixtures of Linear Models
We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images are classified by evaluating their log-likelihoods under each model. We use an EM-based algorithm in which the M-step is computationally straightforward principal componentsanalysis(PCA). Incorporating tangent-planeinformation [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance.
Geoffrey E. Hinton, Michael Revow, Peter Dayan
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1994
Where NIPS
Authors Geoffrey E. Hinton, Michael Revow, Peter Dayan
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