The first paradigm used binary classification to detect each individual concept in a concept set.
第一个阶段使用二值分类算法检测概念集中的每个概念，并达到了一定得准确度。
Simple SVM can only handle binary classification problems; can not directly handle multi-value classification.
简单的支持向量机只能处理二值分类问题，不能直接处理多值分类问题。
Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.
支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
In this paper, a classification algorithm based on multi-threshold binary encoding for Description of Spectral Shape is introduced, and result of image experimentation is present.
本文提出一种基于光谱形状描述的多门限的二进制编码分类方法，并给出了图象实验的结果。
Produce an algorithm based on encoding binary tree and supporting vector multi-category classification algorithm.
给出了一种基于编码二叉树的支持向量的多类分类算法。
In the first step, the antecedents of fuzzy classification system and input variables are coded into a binary string and treated as an individual in genetic algorithm.
第一阶段，将模糊分类系统的前件和输入变量编码为一个个体，实现了输入变量论域的动态划分和输入变量选择。
The taxonomy of opposition is relatively complicated, and most researches have been focussed on the classification and semantic features of binary opposition.
英语反义词的分类较为复杂，因而大多数涉及到反义词的研究偏重于二项对立关系的分类及语义特征。
A hierarchical decomposed support vector machines binary decision tree is used for classification.
采用一种层次分解的支持向量机二叉决策树进行分类识别。
Taking the objects which the thinking concerned as the basis of classification, the thinking can be divided into unified thinking, binary thinking and multi-thinking.
以思维的对象为分类依据，思维可以分为一元思维、二元思维和多元思维。
Even for binary, linear classification it is data dependent whether it is better to train the geometrical model (SVM?) or a probabilistic one.
即使是二进制的，线性分类它是依赖于数据是否是更好的列车的几何模型(SVM ？)或概率。
In this paper, we present an ear recognition method using PIDC and binary tree SVM classification.
提出了一种基于PIDC和二叉决策树s VM的人耳识别方法。
The problems and defections of the existing methods of SVM multi-class classification were analyzed. A multi-class classification based on binary tree was put forward.
介绍了几种常用的支持向量机多类分类方法，分析其存在的问题及缺点。
Finally, a method based on sub-band feature extraction and Support Vector Machine with Binary Tree Architecture (SVM-BTA) is presented for power quality disturbances multi-classification.
最后，提出了一种基于子频带特征提取和二叉树结构支持向量机相结合的电能质量多分类方法。
Finally, a method based on sub-band feature extraction and Support Vector Machine with Binary Tree Architecture (SVM-BTA) is presented for power quality disturbances multi-classification.
最后，提出了一种基于子频带特征提取和二叉树结构支持向量机相结合的电能质量多分类方法。
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