Zhuo Meng

US Patent:

6907412, Jun 14, 2005

Filed:

Mar 23, 2001

Appl. No.:

09/816909

Inventors:

Zhuo Meng - Cleveland OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06N005/02

US Classification:

706 16, 706 25, 706 18

Abstract:

The subject system provides reduced-dimension mapping of pattern data. Mapping is applied through conventional single-hidden-layer feed-forward neural network with non-linear neurons. According to one aspect of the present invention, the system functions to equalize and orthogonalize lower dimensional output signals by reducing the covariance matrix of the output signals to the form of a diagonal matrix or constant times the identity matrix. The present invention allows for visualization of large bodies of complex multidimensional data in a relatively “topologically correct” low-dimension approximation, to reduce randomness associated with other methods of similar purposes, and to keep the mapping computationally efficient at the same time.

US Patent:

7092922, Aug 15, 2006

Filed:

May 21, 2004

Appl. No.:

10/851630

Inventors:

Zhuo Meng - Broadview Heights OH, US
Baofu Duan - Cleveland Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06E 1/00
G06E 3/00
G06F 15/18
G06G 7/00
G05B 13/02

US Classification:

706 16, 706 12, 706 14, 706 25, 706 31, 700 47, 700 48, 700 50, 703 4, 703 6

Abstract:

An adaptive learning method for automated maintenance of a neural net model is provided. The neural net model is trained with an initial set of training data. Partial products of the trained model are stored. When new training data are available, the trained model is updated by using the stored partial products and the new training data to compute weights for the updated model.

US Patent:

7298906, Nov 20, 2007

Filed:

Feb 28, 2005

Appl. No.:

11/068185

Inventors:

Baofu Duan - Cleveland Heights OH, US
Zhuo Meng - Broadview Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06K 9/46

US Classification:

382195, 382173

Abstract:

A method for feature selection based on hierarchical local-region analysis of feature characteristics in a data set of mixed data type is provided. A data space associated with a mixed-type data set is partitioned into a hierarchy of plural local regions. A relationship metric (for example, a similarity correlation metric) is used to evaluate for each local region a relationship measure between input features and a target. One or more relevant features is identified, by using the relationship measure for each local region.

US Patent:

7313279, Dec 25, 2007

Filed:

Jul 8, 2003

Appl. No.:

10/615885

Inventors:

Baofu Duan - Cleveland Heights OH, US
Zhuo Meng - Broadview Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06K 9/46

US Classification:

382195

Abstract:

Methods for feature selection based on hierarchical local-region analysis of feature relationships in a data set are provided.

US Patent:

7444310, Oct 28, 2008

Filed:

Mar 28, 2003

Appl. No.:

10/401930

Inventors:

Zhuo Meng - Broadview Heights OH, US
Baofu Duan - Cleveland Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06N 5/00

US Classification:

706 15, 706 45

Abstract:

A model maintenance method is provided. If accuracy of prediction by a current model through consultation with new data is determined to be below a predetermined threshold, a compound model is formed by supplementing the current model with a local net trained with the new data.

US Patent:

7483868, Jan 27, 2009

Filed:

Feb 26, 2003

Appl. No.:

10/374406

Inventors:

Zhuo Meng - Broadview Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06F 15/18

US Classification:

706 26, 706 23

Abstract:

Method of incrementally forming and adaptively updating a neural net model are provided. A function approximation node is incrementally added to the neural net model. Function parameters for the function approximation node are determined and function parameters of other nodes in the neural network model are updated, by using the function parameters of the other nodes prior to addition of the function approximation node to the neural network model.

US Patent:

7562054, Jul 14, 2009

Filed:

Jul 9, 2004

Appl. No.:

10/888882

Inventors:

David E. Huddleston - Lakewood OH, US
Ronald J. Cass - Cleveland Heights OH, US
Zhuo Meng - Broadview Heights OH, US
Qian Yang - Broadview Heights OH, US
Xinyu Mao - Sagamore Hills OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06F 15/18
G06N 3/00
G06N 3/12

US Classification:

706 13, 706 14, 706 26

Abstract:

A method for automated feature selection is provided. One or more initial sets of features are generated and evaluated to determine quality scores for the feature sets. Selected ones of the feature sets are (i) chosen according to the quality scores and modified to generate a generation of modified feature sets, (ii) the modified feature sets are evaluated to determine quality scores for the modified feature sets, and (i) and (ii) are repeated until a modified feature set is satisfactory.

US Patent:

7716148, May 11, 2010

Filed:

Apr 18, 2003

Appl. No.:

10/418659

Inventors:

Zhuo Meng - Broadview Heights OH, US
Baofu Duan - Cleveland Heights OH, US
Ronald J Cass - Cleveland Heights OH, US

Assignee:

Computer Associates Think, Inc. - Islandia NY

International Classification:

G06K 9/62

US Classification:

706 26, 706 20, 706 21, 706 25

Abstract:

An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.