Joseph M Collins

US Patent:

62397400, May 29, 2001

Filed:

Jun 15, 1993

Appl. No.:

8/076922

Inventors:

Joseph B. Collins - Washington DC
Jeffrey K. Uhlmann - Alexandria VA

Assignee:

The United States of America as represented by the Secretary of the Navy - Washington DC

International Classification:

G01S 1300
G01S 1353

US Classification:

342109

Abstract:

We describe an efficient algorithm for evaluating the (weighted bipartite graph of) associations between two sets of data with gaussian error, e. g. , between a set of measured state vectors and a set of estimated state vectors. First a general method is developed for determining, from the covariance matrix, minimal d-dimensional error ellipsoids for the state vectors which always overlap when a gating criterion is satisfied. Circumscribing boxes, or d-ranges, for the data ellipsoids are then found and whenever they overlap the association probability is computed. For efficiently determining the intersections of the d-ranges a multidimensional search tree method is used to reduce the overall scaling of the evaluation of associations. Very few associations that lie outside the predetermined error threshold or gate are evaluated. Empirical testing for variously distributed data in both three and eight dimensions indicate that the scaling is significantly reduced from N. sup.

US Patent:

20210326664, Oct 21, 2021

Filed:

Mar 1, 2021

Appl. No.:

17/188923

Inventors:

- Arlington VA, US
Joseph B. Collins - McLean VA, US

International Classification:

G06K 9/62
G06N 20/20

Abstract:

Methods and systems are provided for automatic classification of information in an adversarial machine learning setting. For example, a learner that includes multiple classifiers may perform data classification automatically. Each classifier may be trained with adversarial data of a different strength. For a given query to be classified, the learner is configured to intelligently select a classifier that is commensurate with the adversarial strength of the data inside the query, without explicit knowledge of that data or its adversarial strength.