Agilent Technologies, Inc.

Jonathan Qiang Li

John M Heumann

Daniel A Usikov

Representative embodiments are directed to systems and methods for processing training data for a statistical classification application. In one embodiment, confidence values are calculated for training data elements to identify the probabilities of the training data elements belonging to identified classes. An interactive scatter plot is generated using the calculated confidence values. The scatter plot visually indicates the confidence values of points in the scatter plot. Accordingly, the user is able to identify potentially misclassified training data elements. The user may select training data elements from the scatter plot and reclassify training elements as appropriate. Upon reclassification, the confidence values may be recalculated and the scatter plot revised.

System and method for processing training data for a statistical application

Embodiments of the present invention use the results of a plurality of defect detecting tests and adjust the result of each test in a manner that reflects the accuracy of the test that produced it. A manufactured unit can then be evaluated using a mathematical combination of the adjusted results.

Combining multiple independent sources of information for classification of devices under test

Techniques for detecting temporal process variation and for managing and predicting performance of automatic classifiers applied to such processes using performance estimates based on temporal ordering of the samples are presented.

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Mountain View, CA, United States of America

Jonathan Qiang Li