Machine learning
AssigneeHewlett-Packard Development Company, L.P.
InventorsGeorge Forman, Stephane Chiocchetti
Life of this patent
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Abstract
A first set of classifiers are trained to predict categories using attributes of cases. A second set of a classifiers are also trained to predict the categories using the attributes of the cases. In addition, the second set of classifiers use the predictions from at least one of the classifiers in the first set to make predictions.
Claims20
A method of machine learning comprising:
- training a first set of classifiers to predict a plurality of categories using attributes of cases;
- and
- training a second set of a classifiers to predict the plurality of categories using the attributes of the cases and using the predictions from one or more of the classifiers in the first set.
A method of classifying a case comprising:
- applying the case to each classifier of a first set of classifiers operable to generate predictions for categories;
- applying the case to each classifier of a second set of classifiers also operable to generate predictions for the categories;
- applying outputs of a plurality of classifiers in the first set of classifiers to one or more classifiers in the second set of classifiers;
- and
- determining a category in which the case belongs based on the output of at least one of the second set of classifiers.
An apparatus comprising:
- a plurality of classifiers in a first set, each classifier being operable to predict a category;
- a plurality of classifiers in a second set, each classifier in the second set corresponding to a classifier in the first set such that a classifier in the second set predicts the same category as a corresponding classifier in the first set;
- a training set input into the first set of classifiers, wherein the training set includes attributes of cases and labels;
- and
- each classifier in the second set receiving predictions from at least some of non-corresponding classifiers in the first set such that each classifier in the second set is trained using the predictions from the at least some of the non-corresponding classifiers and the training set.
An apparatus comprising:
- a first set of classifier means for predicting a plurality of categories;
- a second set of classifier means for predicting the plurality of categories, wherein the first set of classifier means and the second set of classifier means receive a same set of cases to predict, and the second set of classifier means also receives predictions from at least some of the first set of classifier means generating predictions for the set of cases.
A computer program provided on a computer readable medium and comprising code that when executed causes a computer to perform the following:
- training a first set of classifiers to predict a plurality of categories using attributes of cases;
- and
- training a second set of a classifiers to predict the plurality of categories using the attributes of the cases and using the predictions from at least one of the classifiers in the first set.
Dependencies are inferred from the claim text.