Feature Extractions and Recognition Methods for Concept Recognition and its Applications.

概念识别的特征提取和识别方法及其应用。

• 批准号: 18500124
• 负责人: TOYAMA Jun
• 金额: $ 2.12万
• 依托单位: Hokkaido University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500124/
• 关键词: Pattern Recognition; Prototype; Feature Selection; Granularity; SDensitivity; Rule extraction; Speech Recognition; 木構造; 概念; 識別子独立

1) Classification of Features: The knowledge that is necessary for judgments are not numerical score like a real number but rough categories. A new concept "granularity" based on the rough categories was introduced. We interpreted discrete data as a grouping problem. Therefore an algorithm for discovering semi-optical answer for a grouping problem was proposed.2) Discovering Prototype: The features that is necessary for judgments are not all knowledge and experiments but some abstract data. From this point of view, an algorithm that discover typical prototype in enormous data was proposed. The prototype is not a point but a object has volume. A prototype update algorithm was also proposed to adapt increasing data with progress of time. On the other hand, we defined the similarity of tree structures. We proposed an algorithm that extracts some typical trees in many trees using the similarity.3) Feature Selection depend on Individual situations: A classifier-independent feature selection method was proposed. On the other hand, a feature selection method that extract optimum features in random selected features was also proposed from a point of view that optimum feature sets are depend on category and/or category set.

1)特征分类：判断所需的知识不是像真实的数字那样的数字分数，而是粗略的类别。在粗糙范畴的基础上引入了粒度的概念。我们将离散数据解释为分组问题。2）发现原型：判断所需的特征不是所有的知识和实验，而是一些抽象的数据。从这个角度出发，提出了一种在海量数据中发现典型原型的算法。原型不是一个点，而是一个有体积的物体。提出了一种原型更新算法，以适应随着时间的推移不断增加的数据。另一方面，我们定义了树结构的相似性。提出了一种利用相似度从多棵树中提取典型树的算法。3）基于个体情况的特征选择：提出了一种与分类器无关的特征选择方法。另一方面，从最优特征集依赖于类别和/或类别集的角度出发，提出了一种从随机选择的特征中提取最优特征的特征选择方法。

项目成果

Model Selection Using a Class of Kernels with an Invariant Metric Structural

使用具有不变度量结构的核类进行模型选择

• 发表时间: 2006
• 期刊: Syntactic and Statistical Pattern Recognition, Lecture Notes in Computer Science 4109
• 作者: Y.Suhara;A.Sakurai;Mineichi Kudo;M. Kudo;Akira Tanaka;A. Tanaka;Y. Muto;N. Abe;N. Abe;A. Tanaka
• 通讯作者: A. Tanaka

木構造データに対する頻出類似部分木の発見

查找树结构数据的频繁相似子树

• 发表时间: 2007
• 作者: Y.Suhara;A.Sakurai;Mineichi Kudo;M. Kudo;Akira Tanaka;A. Tanaka;Y. Muto;N. Abe;N. Abe;A. Tanaka;Y. Muto;N. Abe;A. Tanaka;Y.Muto;N.Abe;N.Abe;A.Tanaka;M.Yamada;A.Nakamura;Akira Tanaka;A. Tanaka;外山 淳;J. Toyama;Hisashi Tosaka;H. Tosaka;中村 篤祥;A. Nakamura;林 真吾;S. Hayashi;神田 勇介;Y. Kanda;紙谷 一啓;Yohji Shidara;Yohji Shidara;白井 賢志;佐藤 麻衣子;S. Shirai;M. Sato;戸坂 央
• 通讯作者: 戸坂 央

Finding of Frequent Similar Subtrees in Tree-Structured Data.

在树结构数据中查找频繁的相似子树。

• 发表时间: 2007
• 作者: Y.Suhara;A.Sakurai;Mineichi Kudo;M. Kudo;Akira Tanaka;A. Tanaka;Y. Muto;N. Abe;N. Abe;A. Tanaka;Y. Muto;N. Abe;A. Tanaka;Y.Muto;N.Abe;N.Abe;A.Tanaka;M.Yamada;A.Nakamura;Akira Tanaka;A. Tanaka;外山 淳;J. Toyama;Hisashi Tosaka;H. Tosaka;中村 篤祥;A. Nakamura;林 真吾;S. Hayashi;神田 勇介;Y. Kanda;紙谷 一啓;Yohji Shidara;Yohji Shidara;白井 賢志;佐藤 麻衣子;S. Shirai;M. Sato;戸坂 央;H. Tosaka
• 通讯作者: H. Tosaka

Integrated kernels and their properties

• DOI: 10.1016/j.patcog.2007.02.014
• 发表时间: 2007-11
• 期刊: Pattern Recognit.
• 作者: A. Tanaka;H. Imai;Mineichi Kudo;M. Miyakoshi

部分クラスの複雑さと列挙法について.

关于部分类和枚举方法的复杂性。

• 发表时间: 2007
• 作者: Y.Suhara;A.Sakurai;Mineichi Kudo;M. Kudo;Akira Tanaka;A. Tanaka;Y. Muto;N. Abe;N. Abe;A. Tanaka;Y. Muto;N. Abe;A. Tanaka;Y.Muto;N.Abe;N.Abe;A.Tanaka;M.Yamada;A.Nakamura;Akira Tanaka;A. Tanaka;外山 淳;J. Toyama;Hisashi Tosaka;H. Tosaka;中村 篤祥
• 通讯作者: 中村 篤祥