System and method for inducing a top-down hierarchical categorizer
AssigneeHewlett-Packard Development Company, L.P.
InventorsGeorge H. Forman, Henri Jacques Suermondt
Life of this patent
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Abstract
A method of inducing a top-down hierarchical categorizer includes providing a set of labeled training items. Each labeled training item includes an associated label representing a single category assignment for the training item. A set of unlabeled training items is provided. A prior is associated with the set of unlabeled training items that is independent of any particular feature contained in the unlabeled training items. The prior represents a plurality of possible category assignments for the set of unlabeled training items. A top-down hierarchical categorizer is induced with a machine learning algorithm based on the set of labeled training items, the set of unlabeled training items, and the prior.
Claims29
A method for inducing a top-down hierarchical categorizer, comprising:
- providing a set of labeled training items, each labeled training item including an associated label representing a single category assignment for the training item;
- providing a set of unlabeled training items;
- associating a prior with the set of unlabeled training items that is independent of any particular feature contained in the unlabeled training items, the prior representing a plurality of possible category assignments for the set of unlabeled training items;
- and
- inducing a top-down hierarchical categorizer with a machine learning algorithm based on the set of labeled training items, the set of unlabeled training items, and the prior.
A system for inducing a top-down hierarchical categorizer based on labeled training items and unlabeled training items, each labeled training item including a plurality of features and a label identifying a single category assignment, each unlabeled training item including a plurality of features and prior information representing a plurality of possible category assignments for the unlabeled training item, the system comprising:
- a plurality of categories organized in a hierarchy of categories;
- and
- an inducer for inducing a plurality of categorizers corresponding to the plurality of categories, the inducer configured to induce each categorizer based on the features of labeled training items assigned to categories under that categorizer and based on the features of unlabeled training items with prior information representing category assignments that map to a category under that categorizer.
A system for inducing a top-down hierarchical categorizer from a set of labeled training items and a set of unlabeled training items, each labeled training item including an associated label representing a single category assignment for the training item, the system comprising:
- means for associating a prior with each unlabeled training item that is independent of any particular feature contained in the unlabeled training item, the prior representing a plurality of possible category assignments for the unlabeled training item;
- and
- means for inducing a plurality of categorizers based on a machine learning algorithm, the set of labeled training items, the set of unlabeled training items, and the priors associated with the unlabeled training items, the plurality of categorizers including a root categorizer and a plurality of sub-categorizers organized in a tree structure.
A computer-readable medium having computer-executable instructions for performing a method of inducing a top-down hierarchical categorizer based on labeled training items and unlabeled training items, each labeled training item including a label identifying a single category assignment, each unlabeled training item including prior information representing a plurality of possible category assignments for the unlabeled training item, the method comprising:
- providing a plurality of categories organized in a hierarchy of categories;
- inducing a plurality of categorizers corresponding to the plurality of categories;
- and
- associating a set of features with each category based on features of labeled training items assigned to that category and based on features of unlabeled training items with prior information representing category assignments that map to that category.
Dependencies are inferred from the claim text.