System and method for learning stable models
AssigneeYahoo Assets LLC
InventorsNoa Avigdor-Elgrabli, Ran Wolff
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Abstract
The present teaching relates to learning a model. Supervised training data with samples having feature values and a label is received. Unlabeled data be classified is received having samples with values of the same features. Un-stationary features in the supervised training data are detected based on respective feature values from the supervised training data and the unlabeled data. If un-stationary feature exists, adjusted training data set is created based on the supervised training data and the un-stationary features and used to train a stationary classification model. Otherwise, the supervised training data is used to train the stationary classification model.
Claims20
A method, comprising:
- receiving supervised training data set, having data samples each of which includes values of corresponding features and a label representing a classification of the data sample;
- receiving unlabeled data set, having data samples each of which includes values of the corresponding features and is to be classified;
- detecting whether any of the corresponding features is un-stationary in the supervised training data set based on values of features in the supervised training data set and values of features in the unlabeled data set to obtain an un-stationary feature detection result;
- if the un-stationary feature detection result indicates that un-stationary feature exists,
- generating an adjusted supervised training data set based on the supervised training data set according to the un-stationary feature detection result,
- training, via machine learning, a stationary classification model based on the adjusted supervised training data set;
- and
- if no un-stationary feature exists, training, via machine learning, the stationary classification model based on the supervised training data set.
A machine-readable and non-transitory medium having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the following steps:
- receiving supervised training data set, having data samples each of which includes values of corresponding features and a label representing a classification of the data sample;
- receiving unlabeled data set, having data samples each of which includes values of the corresponding features and is to be classified;
- detecting whether any of the corresponding features is un-stationary in the supervised training data set based on values of features in the supervised training data set and values of features in the unlabeled data set to obtain an un-stationary feature detection result;
- if the un-stationary feature detection result indicates that un-stationary feature exists,
- generating an adjusted supervised training data set based on the supervised training data set according to the un-stationary feature detection result,
- training, via machine learning, a stationary classification model based on the adjusted supervised training data set;
- and
- if no un-stationary feature exists, training, via machine learning, the stationary classification model based on the supervised training data set.
A system, comprising:
- an un-stationary feature detector implemented by a processor and configured for
- receiving supervised training data set, having data samples each of which includes values of corresponding features and a label representing a classification of the data sample,
- receiving unlabeled data set, having data samples each of which includes values of the corresponding features and is to be classified, and
- detecting whether any of the corresponding features is un-stationary in the supervised training data set based on values of features in the supervised training data set and values of features in the unlabeled data set to obtain an un-stationary feature detection result;
- and
- a supervised stationary model training engine implemented by a processor and configured for:
- if the un-stationary feature detection result indicates that un-stationary feature exists,
- generating an adjusted supervised training data set based on the supervised training data set according to the un-stationary feature detection result,
- training, via machine learning, a stationary classification model based on the adjusted supervised training data set, and
- if no un-stationary feature exists, training, via machine learning, the stationary classification model based on the supervised training data set.
Dependencies are inferred from the claim text.