Relevance vector machine

Patent 6633857 Issued on October 14, 2003. Estimated Expiration Date:

September 4, 2019. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Abstract Claims Description Full Text

Patent References

Method for classifying test subjects in knowledge and functionality states
Patent #: 5855011
Issued on: 12/29/1998
Inventor: Tatsuoka

Method for interacting with a test subject with respect to knowledge and functionality Patent #: 6301571
Issued on: 10/09/2001
Inventor: Tatsuoka

Inventor

Tipping, Michael

Assignee

Microsoft Corporation

Application

No. 09/391093 filed on 09/04/1999

US Classes:

706/16, Learning task706/20, Classification or recognition706/25Learning method

Examiners

Primary: Patel, Ramesh
Assistant: Holmes, Michael B.

Attorney, Agent or Firm

Amin & Turocy, LLP

International Classes

G06N 3/00 (20060101)
G06N 3/04 (20060101)

Abstract

A relevance vector machine (RVM) for data modeling is disclosed. The RVM is a probabilistic basis model. Sparsity is achieved through a Bayesian treatment, where a prior is introduced over the weights governed by a set of hyperparameters. As compared to a Support Vector Machine (SVM), the non-zero weights in the RVM represent more prototypical examples of classes, which are termed relevance vectors. The trained RVM utilizes many fewer basis functions than the corresponding SVM, and typically superior test performance. No additional validation of parameters (such as C) is necessary to specify the model, except those associated with the basis.

Other References

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