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 Title: |
US5649070:
Learning system with prototype replacement
[ Derwent Title ]
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| Country: |
US United States of America
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| Inventor: |
Connell, Jonathan Hudson; Cortlandt-Manor, NY
Mohan, Rakesh; Stamford, CT
Bolle, Rudolf Maarten; Bedford Hills, NY
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| Assignee: |
International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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| Published / Filed: |
1997-07-15
/ 1995-02-17
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| Application Number: |
US1995000394525
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| IPC Code: |
Advanced:
G06F 9/44;
G06F 12/00;
G06F 19/00;
G06K 9/62;
G06N 5/04;
Core:
G06N 5/00;
more...
IPC-7:
G06F 17/00;
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| ECLA Code: |
G06K9/62C1D1;
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| U.S. Class: |
Current:
706/014;
706/012;
706/058;
Original:
395/077;
395/068;
395/054;
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| Field of Search: |
395/054,68,77,20,10
382/110,155,159,160,209,224,225
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| Priority Number: |
| 1995-02-17 |
US1995000394525 |
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| Abstract: |
One or more sets of prototype descriptions for a number of classes of objects stored on a computer database are maintained. These prototypes are used as a basis for identifying the class of a presented object. A trainer determines when a new prototype is required to be added to the database based on current match results. This allows the system to be trained to recognize items in classes that deviate significantly from the items that were initially used to determine the classification rules. A determination is made about which prototypes can be deleted on the basis of their match histories. This allows the system to automatically optimize itself to work with a bounded collection of prototypes. In addition, it allows the system to track variations in class characteristics over time and adjust the corresponding set of prototypes appropriately.
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| Attorney, Agent or Firm: |
Whitham, Curtis, Whitham & McGinn ;
Percello, Louis J. ;
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| Primary / Asst. Examiners: |
Hafiz, Tariq R.;
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| INPADOC Legal Status: |
Show legal status actions
Family Legal Status Report
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| Designated Country: |
DE FR GB
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| Family: |
Show 4 known family members
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First Claim:
Show all 15 claims |
We claim:
1. An adaptive classifier apparatus comprising:
- means for scanning a test object and outputting a parameter signal representing a scanned physical parameter of the test object;
- means for receiving said parameter signal and for generating a test feature data based on said parameter signal;
- means for retrievably storing a first plurality of a prototype feature data and a second plurality of a prototype feature data;
- classifying means for comparing said test feature data to each of said first plurality and to each of said second plurality of prototype feature data and for generating a classifier data indicating which of said first plurality and said second plurality has a prototype feature data comparing closest to said test feature data;
- means for generating an event signal associated with said generating a classifier data;
- means for generating a usefulness data corresponding to each of said first plurality and said second plurality of prototype feature data, said usefulness data representing a frequency and recency relative to said event signal that its associated prototype feature datum is the prototype feature datum comparing closest with the test feature;
- means for modifying said stored first plurality of prototype feature data and said stored second plurality of prototype feature data based on said usefulness data.
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| Background / Summary: |
Show background / summary
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| Drawing Descriptions: |
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| Description: |
Show description
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| Forward References: |
Show 18 U.S. patent(s) that reference this one
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| Foreign References: |
None
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| Other Abstract Info: |
DERABS G1996-373076
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| Other References: |
Chinrungrueng, C. & Sequin, C.H., "Optimal Adaptive K-Means Algorithm with Dynamic Adjustment of Learning Rate", IEEE Transactions on Neural Networks, vol. 6, No. 1, pp. 157-169 Jan. 1995.
(13 pages)
Cited by 2 patents
[ISI abstract]
Chinrungrueng, C. & Sequein, C.H., "Optimal Adaptive K-Means Algorithm with Dynamic Adjustment of Learning Rate", IEEE International Conference on Neural Networks, vol. I, pp. 855-862 1991.
Darken, C. & Moody, J., "Fast Adaptive K-Means Clustering: Some Empirical Results", IEEE International Conference on Neural Networks, vol. II, pp. 233-238 1990.
Deitel, H.M., Operating Systems, Addison-Wesley:New York, pp. 251-273 1990.
"Produce Recognition System", Application No. 08/235/834, filed Apr. 29, 1994.
R.S. Michalski et al, "NSF/DARPA Workshop on Machine Learning and Vision: A Summary", Proceedings of the DARPA Image Understanding Workshop, P. 351, Apr. 1993.
R.S. Michalski, "Pattern Recognition as Rule-Guided Inductive Inference", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, No. 4, Jul. 1980, pp. 349-361.
(13 pages)
R.P. Lippmann, "An Introduction to Computing with Neural Nets", IEEE 22. Acoustics, Speech, and Signal Processing Magazine, Apr. 1987, pp. 4-22.
J. Ross Quinlan, "Learning Efficient Classification Procedures and Their Application to Chess End Games", Machine Learning: An Aritifical Intelligence Approach, Tioga Publishing, Palo Alto CA, 1983, pp. 463-482.
P.H. Winston, "Learning Class Description from Samples", Artificial Inteligence (2nd Ed.), Addison-Wesley, 1984, pp. 391-414.
R.O. Duda et al, "Nonparametric Techniques", (Chapter 4), Pattern Classification and Scene Analysis, 1973, pp. 85-129.
A.K. Jain et al, "Algorithms for Clustering Data", Prentice Hall, 1988, pp. 88-103.
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