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集成分类器
相关语句
  ensemble classifier
     The Application of an Ensemble Classifier of Artificial Neural Networks in TSA
     一种神经网络集成分类器在暂态稳定评估中的应用
短句来源
  integrated classifier
     In this paper, after analyzing the classification performance of MLFN and discrimination analysis, a novel integrated classifier MLFN-CCA-Fisher is proposed.
     分析了各单一方法的分类性能,提出了神经网络与统计方法相集成的策略,由此提出MLFN-CCA-Fisher集成分类器
短句来源
     Integrated Classifier Based on Artificial Neural Network with Multivariable Statistics and Its Application
     基于网络与统计方法的集成分类器及其应用
短句来源
     Applying the new integrated classifier,the results is satisfying not only in the performance test but also in the practical applications.
     在性能测试与实际应用中,集成分类器均取得了良好的效果。
短句来源
  integration classifier
     Multiple neural network integration classifier of parallel structure based on DSP
     基于DSP并行结构的多神经网络集成分类器
短句来源
  “集成分类器”译为未确定词的双语例句
     Application of Boosting-based Decision Tree Ensemble Classifiers for Discrimination of Thermophilic and Mesophilic Proteins
     基于Boosting机制的决策树集成分类器识别嗜热和常温蛋白
短句来源
     The Study and Development of Integrated Chemical Pattern Classifiers
     化学模式集成分类器的研究与开发
短句来源
     A linear support vector machine was trained using the component abstracted by the NLCCA, then an integrated NLCCA LSVC classifier was got and it has good prediction performance .
     由此构建的NLCCA LSVC集成分类器具有优良的预测性能。
短句来源
     The proposed spectrum-shape parameter is anti-noise,while the new spectrum-line one is robust against modulation parameters,and the performance of neural network ensemble is much better than that of a single network classifier. Simulation results show that the recognition rate can reach 94% when SNR is above 5dB.
     仿真表明,谱形状特征参数具有很好的抗噪声能力,离散谱线特征参数对信号调制参数更加稳健性,神经网络集成分类器的识别性能显著优于单个网络分类器,SNR>5dB时该识别方法的总体识别率在94%以上.
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      ensemble classifier
    Recent interest in the classification process has focused on ensemble classifier systems.
          
    Using ensemble classifier to identify membrane protein types
          
    In this paper, a novel classifier, the so-called "ensemble classifier", was introduced.
          
    It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures.
          
    It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.
          
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      integrated classifier
    Design of a new type of integrated classifier for network intrusion detection systems
          
    Based on the analysis of the network intrusion detection model, a new design scheme for the integrated classifier is proposed.
          
    Experimental results obtained from a VLSI modulator with integrated classifier, trained to produce stable noise shaping modulation of orders one and two, are presented.
          
    Some experimental results using telephone speech databases are presented to demonstrate the potential of this hybrid integrated classifier.
          


    As a high level integration approach, metasynthesis has drawn much attention. In this paper, a metasynthetic approach for combination of multiple classifiers is proposed. As a first step, a linear integration model is built. In this model, not only the degree of the similarity between the input and its ranked candidates are appled, but also the supporting effects of multiple candidates are taken into account, and their contributions to the integration result is expressed by a linear function. Then, an algorithm...

    As a high level integration approach, metasynthesis has drawn much attention. In this paper, a metasynthetic approach for combination of multiple classifiers is proposed. As a first step, a linear integration model is built. In this model, not only the degree of the similarity between the input and its ranked candidates are appled, but also the supporting effects of multiple candidates are taken into account, and their contributions to the integration result is expressed by a linear function. Then, an algorithm for automatically calculating the arguments of the model through supervised learning is provided. Experiments on metasynthesis of three individual classifiers for handwritten Chinese character recognition are given to demonstrate the effectiveness of our method.

    根据多信源信息处理与字符识别的经验知识,提出了一个识别手写汉字的多分类器线性集成模型.这个模型不仅考虑到不同的分类器对不同字符识别能力的不同,而且还考虑了不同的分类器得出的输入字符与参考模板之间相似度的实际大小对判决的影响,及不同分类器提供的候选字符对判决的支持作用,更重要的是提供了一种通过监督学习,利用计算机程序自动计算模型参数的方法,因而实现了一个较好的集成系统.同时,本文还提供了三个用于集成的分类器,它们集成的结果充分显示了本方法的有效性。

    An approach for automatic recognition of a vehicle license using color segmentation and hierarchical hybird integrated classifier is presented. This approach consists of color segmentation, object locating, character recognition and post process modules. A multi layer perceptron networks (MLPN) is employed for stable color segmentation. Projection method is used to locate vehicle license plate based on the prior knowledge of fixed ratio of horizontal and vertical length of a plate and to extract characters...

    An approach for automatic recognition of a vehicle license using color segmentation and hierarchical hybird integrated classifier is presented. This approach consists of color segmentation, object locating, character recognition and post process modules. A multi layer perceptron networks (MLPN) is employed for stable color segmentation. Projection method is used to locate vehicle license plate based on the prior knowledge of fixed ratio of horizontal and vertical length of a plate and to extract characters in the plate. A hierarchical hybrid integrated classifier is used to recognize these characters, then a license database is used to verify recognition result given by hierarchical hybrid integrated classifier in order to improve the reliability of the recognition results. The experimental result show that the proposed approach is excellent in accuracy and robustness, the proper license plate locating rate is above 98.6% and the character recognition accuracy reaches 95%, and can be put into practical use.

    提出一种采用彩色分割及多级混合集成分类器的车牌自动识别方法.该方法由彩色分割、目标定位、字符识别及后处理模块组成.采用多层感知器网络(MLPN)对输入彩色图象进行彩色分割,通过投影法分割出潜在的车牌区域并进一步切割出字符,由多级混合集成分类器给出字符识别的初步识别结果及置信度,经后处理得到最终结果.该方法识别正确率高、鲁棒性好,车牌定位正确率达98.6%,字符识别正确率达到95%以上,具有很好的实用技术指标.

    Presented in this paper is an estimating method of multi\|feature and multi\|classifier combination based on the posterior probability estimators. Also presented is a method to extract effective discriminant features of handwritten digits based on a set of uncorrelated optimal discriminant features and KL transform. Experiments have been performed with Concordia University CENPARMI's handwritten digit database based on the nearest\|neighbor distance classifier and the nearest\|neighbor correlation classifier,...

    Presented in this paper is an estimating method of multi\|feature and multi\|classifier combination based on the posterior probability estimators. Also presented is a method to extract effective discriminant features of handwritten digits based on a set of uncorrelated optimal discriminant features and KL transform. Experiments have been performed with Concordia University CENPARMI's handwritten digit database based on the nearest\|neighbor distance classifier and the nearest\|neighbor correlation classifier, and 12 features of handwritten digits. Experimental results show that the estimating method is better than the polling method or the counting method respectively and the recognition rate of the estimating method is as high as 97%. A new combination classifier is finally brought forward, which is based on the strict structure classifier and the estimating method. Better experimental results have been obtained by means of this new combination classifier: the recognition rate, the reject rate, and the reliability are as high as 97.15%, 2.05%, and 99.18% respectively, which are the best results up to now on the handwritten digit database.

    文中提出了基于后验概率估计的多特征多分类器组合识别的估计法,并提出了基于具有统计不相关性的最佳鉴别变换与KL变换抽取手写体数字的有效鉴别特征的方法.实验采用Concordia University CENPARMI手写体数字数据库.用最近邻距离分类器与最近邻相关分类器这两个分类器,对手写体数字的12 个特征做多特征多分类器组合识别实验. 实验结果表明:估计法优于常用的投票法与计分法,估计法的识别率高达97% .本文最后基于一个严格的结构分类器与估计法提出了一个集成分类器,该集成分类器获得了更好的实验结果:识别率、拒识率与可靠性分别可达到97.15% 、2.05% 、99.18% ,这是目前在该手写体数字数据库上所得到的最好的实验结果.

     
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