US 2014/0180978 A1 · INSTANCE WEIGHTED LEARNING MACHINE LEARNING MODEL

Abstract

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model may include identifying a temporal sequence of reinforcement learning machine learning training instances with each of the training instances including a state-action pair, determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, associating the first quality value with the first training instance, and associating the second quality value with the second training instance. In this example embodiment, the first quality value is higher than the second quality value.

Claims20

Dependent claims

A method of employing an instance weighted learning (IWL) machine learning model, the method comprising:

identifying a temporal sequence of reinforcement learning machine learning training instances, each of the training instances including a state-action pair;
determining a first quality value for a first training instance in the temporal sequence of reinforcement learning machine learning training instances;
determining a second quality value for a second training instance in the temporal sequence of reinforcement learning machine learning training instances, the first quality value being higher than the second quality value;
associating the first quality value with the first training instance;
and
associating the second quality value with the second training instance.

3 more elements

A method of employing an instance weighted learning (IWL) machine learning model to train a classifier, the method comprising:

identifying a set of machine learning training instances;
determining a first quality value for a first training instance in the set of machine learning training instances;
determining a second quality value for a second training instance in the set of machine learning training instances, the first quality value being higher than the second quality value;
associating the first quality value with the first training instance;
associating the second quality value with the second training instance;
training a classifier using the first training instance weighted using the first quality value;
and
training the classifier using the second training instance weighted using the second quality value,
wherein the training of the classifier is influenced more by the first training instance than by the second training instance due to the first quality value being higher than the second quality value.

6 more elements

A method of employing an instance weighted learning (IWL) machine learning model to train a classifier, the method comprising:

identifying a temporal sequence of machine learning training instances;
determining a first quality value for a first training instance in the temporal sequence of machine learning training instances;
determining a second quality value for a second training instance in the temporal sequence of machine learning training instances, the first quality value being higher than the second quality value;
associating the first quality value with the first training instance;
associating the second quality value with the second training instance;
training a classifier using the first training instance weighted with a first weighting factor that is a function of the first quality value;
training the classifier using the second training instance weighted with a second weighting factor that is a function of the second quality value;
wherein the training of the classifier is influenced more by the first training instance than by the second training instance due to the first quality value being higher than the second quality value.

5 more elements

Dependencies are inferred from the claim text.

Instance weighted learning machine learning model

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Instance weighted learning machine learning model

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Abstract

Claims20