The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 10, 2015
Filed:
Jun. 08, 2011
Kazunori Anayama, Tokyo, JP;
Tetsuya Yadoguchi, Tokyo, JP;
Toshiyuki Suzuma, Tokyo, JP;
Eiji Honda, Tokyo, JP;
Takahiro Okada, Tokyo, JP;
Shinya Yoshikawa, Tokyo, JP;
Tamotsu Nishimine, Tokyo, JP;
Kazunori Anayama, Tokyo, JP;
Tetsuya Yadoguchi, Tokyo, JP;
Toshiyuki Suzuma, Tokyo, JP;
Eiji Honda, Tokyo, JP;
Takahiro Okada, Tokyo, JP;
Shinya Yoshikawa, Tokyo, JP;
Tamotsu Nishimine, Tokyo, JP;
Nippon Steel & Sumitomo Metal Corporation, Tokyo, JP;
Abstract
The present invention has its objective to provide a defect classification apparatus which suppresses over-fitting and accurately classify the defect type of a defect. A defect classification apparatus is provided in which a data point indicating feature information of a defect to be classified having an unknown defect type is mapped to a point in a mapping space having a dimensional number higher than the number of features constituting the feature information, and the defect type of the defect to be classified is classified based on in which of two regions of defect type, which are formed by separating the mapping space by a decision boundary, the mapped point is located, wherein a discriminant function indicating the decision boundary is determined by adopting a weight which minimizes the sum of the classification error, which corresponds to the accuracy in classifying a training defect dataset, and a regularization term, which has a positive correlation with the dimensional number of the decision boundary, as the weight for each feature constituting the discriminant function.