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向量分类器
相关语句
  vector classifier
     Support Vector Classifier Based PCA with Application to Process
     基于支持向量分类器PCA方法及其在过程监控和故障检测中的应用
短句来源
     To solve the problem of the low sampling efficiency in the contentbased information retrieval, a new classifier named 15-class support vector classifier (15 SVC) is proposed.
     针对基于内容的信息检索中负样本抽样效率低的问题,设计了1 5类支持向量分类器.
短句来源
     The trained support vector classifier is tested and validated effective. When using C-SVC and RBF kernel,parameters can be adjusted to achieve the optimal effect,while the maximal classification ratio reaches 96.67 %.
     对训练好的支持向量分类器进行测试,效果良好,当采用C-SVC,RBF核时调整参数可以得到最优分类效果,最高分类率可达到96.67%。
短句来源
     Micro-calcification detection algorithm based on fast double-layer support vector classifier with reject performance
     基于快速可拒识-双层支持向量分类器的微钙化点的检测算法
短句来源
     Firstly the first layer of support vector classifier(SVC) with maximum margin between two classes will be used for classifying the input pattern;
     检测时,首先利用基于最大间隔超平面的支持向量分类器(SVC)对输入模式进行分类判决;
短句来源
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  vector classifiers
     Compared with the traditional support vector classifiers, the experimental results on USPS and CBCL database demonstrate that the new classifier yields a higher detection rate. 
     在美国邮政数据库(USPS数据库)与麻省理工大学人脸数据库(CBCL数据库)上的实验结果表明,与传统的支持向量分类器相比,这种方法能取得更高的检测精度.
短句来源
  “向量分类器”译为未确定词的双语例句
     Analog circuits fault diagnosis based on serial support vector multi-classifier
     基于串行支持向量分类器的模拟电路故障诊断
短句来源
     For the Polynomial kernel SVM,a 3 38% average increment of recognition rate is obtained showing the efficiency of the proposed approach.
     实验结果表明 ,本文方法的汉字识别率较距离分类器有较大提高 ,其中多项式核函数的支持向量分类器 ,识别率平均提高 3 38% ,表明了本文方法的有效性
短句来源
     (4) Based on the analysis of the advantages and disadvantages of traditional MSPC, independent component analysis is presented as well as its combination with support vector machines classifier. Its application in propylene polymerization process has shown the effectiveness of this method.
     (4) 在分析了传统的多变量统计过程控制方法的优缺点之后,将独立成分分析与支持向量分类器相结合,对丙烯聚合过程的监控进行了初步的研究,工业实例表明了该方法的有效性。
短句来源
     Based on the above the first work, the second work of this paper is to further develop a screening algorithm of combing two successive classifiers in a cascade structure which dramatically increases the speed of the screening process.
     首先采用相对简单的阀值法进行大规模的分类,然后对剩余的还不能够确定的区域采用相对复杂的代价敏感的支持向量分类器进行进一步分类,从而在保证精确度的同时,极大地提高了算法执行速度。
短句来源
     Support vector machines(SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also controls VC confidence of discriminant functions,hence a better generalization ability is guaranteed.
     支持向量机作为一种新的机器学习方法 ,由于其建立在结构风险最小化准则之上 ,而不是仅仅使经验风险达到最小 ,从而使得支持向量分类器具有较好的推广能力 .
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  vector classifier
The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way.
      
More precisely, we train a support vector classifier to model the boundary of the space of possible gestures, and train Hidden Markov Models (HMM) on specific gestures.
      
Given a sequence, we can find the start and end of various gestures using a support vector classifier, and find gesture likelihoods and parameters with a HMM.
      
A Fuzzy support vector classifier based on Bayesian optimization
      
We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier.
      
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  vector classifiers
Architectures for detecting and solving conflicts: two-stage classification and support vector classifiers
      
In the event that the first-stage classifiers are not certain about the result, the second-stage system engages a set of support vector classifiers for refining the output hypothesis.
      
Reducing the classification cost of support vector classifiers through an ROC-based reject rule
      
This paper presents a novel reject rule for support vector classifiers, based on the receiver operating characteristic (ROC) curve.
      
Advantages of Unbiased Support Vector Classifiers for Data Mining Applications
      
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A new structure of vector classifiers, based upon space partition of voronot region according to a pattern set gem-rated by classed patterns training, is presented. The fact that this structure is an optimal pattern classifier on Euclidean distance is shown. Two decision rules and three kinds of M classes and k-dimension classifier structures on voronoi region are given.

提出基于模式训练的Voronoi划分向量分类器结构,指出该结构是以欧几里得距离为测量的最优模式分类器,给出M类k维(k≥2)Voronoi域分类器的两个判决准则和三种结构。

A new recognition method of handwritten Chinese characters by support vector machine is presented.Support vector machines(SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also controls VC confidence of discriminant functions,hence a better generalization ability is guaranteed.In this paper,the problems to be solved while applying SVM in Chinese character recognition are addressed at first,and then a two stage of recognition scheme is suggested.Finally,experimental...

A new recognition method of handwritten Chinese characters by support vector machine is presented.Support vector machines(SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also controls VC confidence of discriminant functions,hence a better generalization ability is guaranteed.In this paper,the problems to be solved while applying SVM in Chinese character recognition are addressed at first,and then a two stage of recognition scheme is suggested.Finally,experimental results on 1034 categories of Chinese character from 120 sets of samples are given.For the Polynomial kernel SVM,a 3 38% average increment of recognition rate is obtained showing the efficiency of the proposed approach.

本文提出了一种新的基于支持向量机手写汉字识别方法 .支持向量机作为一种新的机器学习方法 ,由于其建立在结构风险最小化准则之上 ,而不是仅仅使经验风险达到最小 ,从而使得支持向量分类器具有较好的推广能力 .本文首先讨论了支持向量机的基本原理 ,然后 ,针对支持向量机识别大类别手写汉字所遇到的特殊问题 ,文章进行了分析和阐述 ,并在此基础上 ,提出了基于最小距离分类器预分类的两级分类策略 .最后 ,针对GB2 312 80的 10 34个汉字类别的 12 0套手写样本 ,进行了实验仿真 .实验结果表明 ,本文方法的汉字识别率较距离分类器有较大提高 ,其中多项式核函数的支持向量分类器 ,识别率平均提高 3 38% ,表明了本文方法的有效性

To solve the problem of the low sampling efficiency in the contentbased information retrieval, a new classifier named 15-class support vector classifier (15 SVC) is proposed. To improve the detection rate, the positive samples are adopted to model the initial boundaries, and the available negative samples are added to refine the boundaries on the basis of keeping a good global generalization performance. The fast training algorithm of the method is also given by contrast with standard sequential minimal optimization....

To solve the problem of the low sampling efficiency in the contentbased information retrieval, a new classifier named 15-class support vector classifier (15 SVC) is proposed. To improve the detection rate, the positive samples are adopted to model the initial boundaries, and the available negative samples are added to refine the boundaries on the basis of keeping a good global generalization performance. The fast training algorithm of the method is also given by contrast with standard sequential minimal optimization. Compared with the traditional support vector classifiers, the experimental results on USPS and CBCL database demonstrate that the new classifier yields a higher detection rate.

针对基于内容的信息检索中负样本抽样效率低的问题,设计了1 5类支持向量分类器.在训练过程中利用正样本对分类线建立初始模型,在保证总体泛化能力的基础上,用所能获得的负样本修正分类线,以提高其检测精度;通过对比标准序列最小优化方法,得到快速训练算法.在美国邮政数据库(USPS数据库)与麻省理工大学人脸数据库(CBCL数据库)上的实验结果表明,与传统的支持向量分类器相比,这种方法能取得更高的检测精度.

 
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