ADAPTING A PARAMETERIZED CLASSIFIER TO AN ENVIRONMENT

• US 20090263010A1
• Filed: 04/18/2008
• Published: 10/22/2009
• Est. Priority Date: 04/18/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Zhang, Cha, Zhang, Zhengyou
• Application Number:
  US12/105,275
• Publication Number:
  US 20090263010A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06V 30/19147 Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 30/19173 Classification techniques
• CPC Class Codes: G06V 30/194 References adjustable by an adaptive method, e.g. learning

Abstract

A classifier is trained on a first set of examples, and the trained classifier is adapted to perform on a second set of examples. The classifier implements a parameterized labeling function. Initial training of the classifier optimizes the labeling function'"'"'s parameters to minimize a cost function. The classifier and its parameters are provided to an environment in which it will operate, along with an approximation function that approximates the cost function using a compact representation of the first set of examples in place of the actual first set. A second set of examples is collected, and the parameters are modified to minimize a combined cost of labeling the first and second sets of examples. The part of the combined cost that represents the cost of the modified parameters applied to the first set is calculated using the approximation function.

Claims

1. One or more computer-readable storage media comprising executable instructions to perform a method, the method comprising:

classifying a first input item based on a first set of parameters that have been determined based on minimization of a first cost function over a set of first examples;

receiving a second example;

calculating a second set of parameters that minimizes a second cost function over said set of first examples and said second example, said second cost function being based on a third cost function that is based (a) on said first cost function, or (b) on a representation of said set of first examples that is smaller in data size than said set of first examples;

generating a label based on a second input item and on said second set of parameters; and

performing an action based on said label.

2. The one or more computer-readable storage media of claim 1, wherein said representation comprises:

a Hessian matrix that is derived from said first cost function and from said set of first examples.

3. The one or more computer-readable storage media of claim 1, wherein said representation comprises a gradient of said first cost function that is derived, in part, from said set of first examples.

4. The one or more computer-readable storage media of claim 1, wherein said third cost function comprises a finite-order Taylor expansion of said first cost function.

5. The one or more computer-readable storage media of claim 1, wherein said second cost function comprises a weighted sum of said third cost function applied to said representation and a fourth cost function applied to said second example.

6. The one or more computer-readable storage media of claim 1, wherein one of said first examples, or said second example, comprises:

(a) a content item, and (b) a label of said content item.

7. The one or more computer-readable storage media of claim 1, wherein one of said first examples, or said second example, comprises:

(a) a first content item, (b) a second content item, and (c) an indication of whether said first content item and said second content item are to be labeled the same as, or differently from, each other.

8. The one or more computer-readable storage media of claim 1, further comprising:

receiving a first frame and a second frame;

choosing a first window in said first frame;

choosing a second window in said second frame based on a tracking algorithm from said second window and said first window;

determining that said a difference between said first window and said second window does not exceed a threshold according to a distance measure; and

creating a third example that comprises;

(a) said first window, (b) said second window, and (c) a positive similarity indication.

9. A method providing a classifier, the method comprising:

calculating a first set of parameters that, when used with the classifier to label a first set of examples, minimizes a first cost function over said first set of examples;

creating a second cost function that is based on said first cost function and a representation of said first set of examples that is smaller than said first set of examples; and

delivering the classifier, said second cost function, a component that minimizes a weighted sum involving said second cost function, and said representation, to an environment in which the classifier will operate.

10. The method of claim 9, wherein said creating of said second cost function comprises:

calculating a gradient of said first cost function that is derived, in part, from said first set of examples, said second cost function comprising said gradient.

11. The method of claim 9, wherein said creating of said second cost function comprises:

calculating a Hessian matrix that is derived from said first cost function and from said first set of examples, said second cost function comprising said Hessian matrix.

12. The method of claim 9, wherein said creating of said second cost function comprises:

deriving a finite-order Taylor expansion from said first cost function, wherein said second cost function comprises said finite-order Taylor expansion.

13. The method of claim 9, wherein said weighted sum further involves a third cost function that is operable to generate a label on an example that is receivable in said environment, wherein either said second cost function, said third cost function, or both said second cost function and said third cost function are multiplied by weights in said weighted sum.

14. The method of claim 9, wherein one of said examples comprises:

(a) an image, and (b) a label of said image.

15. The method of claim 9, wherein one of said examples comprises:

(a) a first image, (b) a second image, and (c) an indication of whether said first image and said second image are to be labeled the same as, or differently from, each other.

16. A system to classify input, the system comprising:

a camera that collects an input item;

a data remembrance component that stores a first set of parameters;

one or more components that receive said input item, that generate a label of said input item based on said first set of parameters, and that generate a second set of parameters by minimizing a cost function that is based on a representation of a first set of examples from which said first set of parameters is derived, said representation being smaller in data size than said first set of examples; and

an output device through which a result based on said label is communicated to a person.

17. The system of claim 16, wherein said representation comprises:

a gradient of said cost function that is derived from said first set of examples.

18. The system of claim 16, wherein said representation comprises:

a Hessian matrix that is derived from said cost function and from said first set of examples.

19. The system of claim 16, wherein said representation comprises:

a finite-order Taylor expansion of said cost function.

20. The system of claim 16, further comprising:

an example creator that receives a first frame and a second frame and that creates a similarity example that is based on a first window of said first frame, a second window of said second frame, and a histogram distance between said first window and said second window.