Learning machine that considers global structure of data
First Claim
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1. 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.
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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.
7 Citations
22 Claims
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1. A method for training a classifier comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer readable medium comprising computer program instructions which, when executed by a processor, define the steps of:
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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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. An apparatus for training a classifier comprising:
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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.
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Specification