K-SVCR.: A support vector machine for multi-class classification
K-SVCR.: A support vector machine for multi-class classification
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DOI：
10.1016/s0925-2312(03)00435-1
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发表时间：
2003-09-01
期刊：
影响因子：
6
通讯作者：
Català, A
中科院分区：
文献类型：
--
作者：
Angulo, C;Parra, X;Català, A
当隐含两类决策机时，多类分类问题通常通过分解和重构过程来解决。在分解阶段，训练数据被分为两类，在几种方式和两类学习机的训练。要为新条目分配类别，机器的输出将在特定的拉取方案中进行评估。本文介绍了一种基于Vapnik支持向量理论的支持向量分类-回归（Support Vector Classification-Regression，K-SVCR）机器，它是一种新的具有三值输出{-1，0，+1}的训练算法。这种新机器通过使用混合分类和回归SV机（SVM）公式，在分解阶段将所有训练数据评估为1对1对其余结构。对于重建，已经设计了考虑正票和负票的特定拉取方案，使得整体学习架构更具容错性，如将展示的那样。(C)2003 Elsevier B. V.保留所有权利。
The problem of multi-class classification is usually solved by a decomposing and reconstruction procedure when two-class decision machines are implied. During the decomposing phase, training data are partitioned into two classes in several manners and two-class learning machines are trained. To assign the class for a new entry, machines' outputs are evaluated in a specific pulling scheme. This article introduces the "Support Vector Classification-Regression" machine for K-class classification purposes (K-SVCR), a new training algorithm with ternary outputs {-1,0,+1} based on Vapnik's Support Vector theory. This new machine evaluates all the training data into a 1-versus-1-versus-rest structure during the decomposing phase by using a mixed classification and regression SV Machine (SVM) formulation. For the reconstruction, a specific pulling scheme considering positive and negative votes has been designed, making the overall learning architecture more fault-tolerant as it will be demonstrated. (C) 2003 Elsevier B.V. All rights reserved.