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MACHINE LEARNING APPARATUS, MACHINE LEARNING METHOD, CLASSIFICATION APPARATUS, CLASSIFICATION METHOD, AND PROGRAM

机译:机器学习设备,机器学习方法,分类设备,分类方法和程序

摘要

PROBLEM TO BE SOLVED: To classify contents to belonging categories accurately.SOLUTION: An image acquisition section 121 of a machine learning apparatus 100 acquires n (n is a natural number equal to or larger than 2) labeled learning images to be used for categorization. A feature vector acquisition section 122 acquires a feature vector indicating characteristics, from each of the n learning images. A vector conversion section 123 converts a feature vector of each of the n learning images to a similar feature vector, on the basis of similarity between learning images. A classification condition learning section 125 learns a classification condition for classifying the n learning images by category, on the basis of the similar feature vector converted by the vector conversion section 123 and a label added to each of the n learning images. A classifying section 126 classifies unlabeled test images by category, according to the classification condition learned by the classification condition learning section 125.SELECTED DRAWING: Figure 1
机译:解决的问题:将内容准确地分类为所属类别。解决方案:机器学习设备100的图像获取部121获取要用于分类的n个标记的学习图像(n是等于或大于2的自然数)。特征向量获取部122从n个学习图像的每一个中获取指示特征的特征向量。向量转换部123基于学习图像之间的相似度,将n个学习图像的每一个的特征向量转换为相似的特征向量。分类条件学习部分125基于由向量转换部分123转换的相似特征向量和添加到n个学习图像的每一个的标签,学习用于对n个学习图像进行分类的分类条件。分类部分126根据分类条件学习部分125学习的分类条件,按类别对未标记的测试图像进​​行分类。

著录项

  • 公开/公告号JP2016091166A

    专利类型

  • 公开/公告日2016-05-23

    原文格式PDF

  • 申请/专利权人 CASIO COMPUT CO LTD;

    申请/专利号JP20140222600

  • 发明设计人 MATSUNAGA KAZUHISA;

    申请日2014-10-31

  • 分类号G06N99;G06F17/30;G06T7;

  • 国家 JP

  • 入库时间 2022-08-21 14:46:53

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