GENERIC CLASSIFICATION SYSTEM

• US 20100049674A1
• Filed: 04/11/2006
• Published: 02/25/2010
• Est. Priority Date: 04/17/2005
• Status: Abandoned Application

• Current Assignee: Rafael Advanced Defense Systems Limited (Government of Israel)
• Original Assignee: Rafael - Armament Development Authority Ltd.
• Inventors: Zohar, Ron, Reinitz, David, Lulav, Ari
• Application Number:
  US11/911,722
• Publication Number:
  US 20100049674A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

A classification system including a training device and one or more classification device for classifying one or more vectors other than training vectors. The training device is for selecting which training classification algorithms best classifies a set of training vectors, and for finding a set of values, of parameters of a generic classification algorithm, that enable the generic classification algorithm to substantially emulate the selected training classification algorithm.

Claims

1. A classification system, comprising:

(a) a training device for;

(i) selecting which one of a plurality of training classification algorithms best classifies a set of training vectors, and

(ii) finding a set of values, of parameters of a generic classification algorithm, that enable said generic classification algorithm to substantially emulate said selected training classification algorithm; and

(b) at least one classification device for classifying at least one vector other than said training vectors, using said generic classification algorithm with said values.

2. The classification system of claim 1, wherein each said at least one classification device is revesibly operationally connectable to said training device.

3. The classification system of claim 1, wherein said training vectors sample a feature space, and wherein said finding is effected by steps including:

(A) resampling said feature space, thereby obtaining a set of resampling vectors; and

(B) classifying said resampling vectors using said training classification algorithm that best classifies said set of training vectors.

4. The classification system of claim 3, wherein said resampling resamples said feature space more densely than said feature space is sampled by said training vectors.

5. The classification system of claim 1, comprising a plurality of said classification devices.

6. The classification system of claim 1, wherein said training device is further operative to dimensionally reduce said set of training vectors prior to said selecting of said training classification algorithm that best classifies said set of training vectors, and wherein each said at least one classification device is further operative to dimensionally reduce said at least one other vector in a like manner prior to said classifying of said at least one other vector.

7. The classification system of claim 1, further comprising:

(c) for each said classification device, a respective memory, for storing said values, that is reversibly operationally connectable to said training device and to said each classification device.

8. The classification system of claim 1, wherein each said classification device includes a mechanism for executing said generic classification algorithm.

9. The classification system of claim 8, wherein said mechanism includes a general purpose processor.

10. The classification system of claim 8, wherein said mechanism includes a nonvolatile memory for storing program code of said generic classification algorithm.

11. The classification system of claim 8, wherein said mechanism includes a field programmable gate array.

12. The classification system of claim 8, wherein said mechanism includes an application-specific integrated circuit.

13. The classification system of claim 1, wherein said generic classification algorithm is a k-nearest-neighbors algorithm.

14. The classification system of claim 1, wherein said training device includes a nonvolatile memory for storing program code for effecting said selecting and said finding.

15. The classification system of claim 14, wherein at least a portion of said program code is included in a dynamically linked library.

16. A classification system, comprising:

(a) a training device for selecting which one of a plurality of classification algorithms best classifies a set of training vectors; and

(b) at least one classification device for classifying at least one vector other than said training vectors, using said selected classification algorithm.

17. The classification system of claim 16, wherein each said at least one classification device includes:

(i) a mechanism for executing said classification algorithms; and

(ii) a memory for storing an indication of which one of said classification algorithms has been selected by said training device.

18. The classification system of claim 17, wherein said memory is also for storing at least one parameter of said classification algorithm that has been selected by said training device.

19. The classification system of claim 16, wherein each said at least one classification device includes a mechanism for executing said classification algorithms, and wherein the classification system further comprises:

(c) for each said classification device, a respective memory, for storing an indication of which one of said classification algorithms has been selected by said training device, that is reversibly operationally connectable to said training device and to said each classification device.

20. The classification system of claim 19, wherein said respective memory is also for storing at least one parameter of said classification algorithm that has been selected by said training device.