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US 2007/0168305 A1Published application
Learning machine that considers global structure of data
AssigneeNEC Laboratories America, Inc.
InventorsValdimir Vapnik, Michael Miller
Life of this patent
Estimating the term…
Abstract
A new machine learning technique is herein disclosed which generalizes the support vector machine framework. A separating hyperplane in a separating space is optimized in accordance with generalized constraints which are dependent upon the clustering of the input vectors in the dataset.
Claims22
A method for training a classifier comprising:
- receiving input vectors from a dataset that have been clustered into one or more clusters;
- specifying generalized constraints which are dependent upon the clusters of input vectors in the dataset;
- and
- optimizing a separating hyperplane in a separating space subject to the generalized constraints, where the input vectors are mapped to the separating space using a kernel function and where the separating hyperplane is determined by a set of coefficients generated in accordance with the generalized constraints.
1 more element
A computer readable medium comprising computer program instructions which, when executed by a processor, define the steps of:
- receiving input vectors from a dataset that have been clustered into one or more clusters;
- specifying generalized constraints which are dependent upon the clusters of input vectors in the dataset;
- and
- optimizing a separating hyperplane in a separating space subject to the generalized constraints, where the input vectors are mapped to the separating space using a kernel function and where the separating hyperplane is determined by a set of coefficients generated in accordance with the generalized constraints.
1 more element
An apparatus for training a classifier comprising:
- means for receiving input vectors from a dataset that have been clustered into one or more clusters;
- means for generating coefficients which determine a separating hyperplane in a separating space, the input vectors being mapped into the separating space, and the coefficients being optimized in accordance with generalized constraints which are dependent upon the clusters of input vectors in the dataset.
Dependencies are inferred from the claim text.