Supervised learning using multi-scale features from time...

Details Supervised learning using multi-scale features from time... Supervised learning using multi-scale features from time...

2011-08-16

2011-08-16

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Machine learning

Reexamination Certificate

active

08001062

ABSTRACT:
Disclosed herein is a method, a system and a computer program product for generating a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event. Initially, a set of labeled time series events is received. A set of time series features is identified for a selected set of the labeled time series events. A plurality of scale space decompositions is generated based on the set of time series features. A plurality of multi-scale features is generated based on the plurality of scale space decompositions. A first subset of the plurality of multi-scale features that correspond at least in part to a subset of space or time points within a time series event that contain feature data that distinguish the time series event as belonging to a class of time series events that corresponds to the class label are identified. A statistical classification model for classifying an unlabeled time series event based on the class corresponding with the class label is generated based at least in part on the at the first subset of the plurality of multi-scale features.

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