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boosting algorithm
相关语句
  boosting算法
     Application of Boosting Algorithm to Auto Text Categorization
     Boosting算法在文本自动分类中的应用
短句来源
     Boosting Algorithm's Applications in Distance Education Analysis and Image Classification
     Boosting算法在远程教育分析和图像分类中的应用研究
短句来源
     The application of Boosting algorithm in chemical data mining
     Boosting算法及其在化学数据挖掘中的应用
短句来源
     AdaBoost, that is adaptive boosting algorithm, may improve the performance of any given classifier.
     AdaBoost即自适应Boosting算法,它可以提高任意给定分类器的分类精度。
短句来源
     Thus in BLIR system, a large number of 2D-MHMM models are generated and a boosting algorithm is used to associate keyword with models.
     BLIR系统首先生成大量的2D-MHMM模型,然后用Boosting算法来实现关键词与2D-MHMM模型的关联。
短句来源
更多       
  boosting方法
     Face Detection Based on Boosting Algorithm
     基于Boosting方法的人脸检测
短句来源
     Face detection based on cascaded boosting algorithm
     基于级联式Boosting方法的人脸检测
短句来源
     The Boosting algorithm, as one of the ensemble methods, fascinates many researchers with its nearly "perfect" classification capacity. In this research, we first introduced the idea of Boosting, described two fundamental procedures, AdaBoost and LogitBoost.
     这类“小样本、高维度”的资料(m>>n),给传统的分类判别方法带来了前所未有的挑战,Boosting方法作为集成算法中的一员,一直以其“完美”的分类能力吸引着众多的研究者和应用者。
短句来源
     Firstly, it builds a function for intrusion detection using neural network, then uses boosting algorithm and builds a serials of function based on the neural network. At last, a final function based on boosted combination of those functions is found, which is applied in intrusion detection.
     先用神经网络初步确定一个入侵检测函数,在此基础上,利用Boosting方法构造一个基于神经网络的入侵检测函数序列,然后以一定的方式将它们组合成一个加强的总检测函数,据此进行入侵检测。
短句来源
     In order to improve the precision of the RBF neural network Models of reservoir regulation, we use Boosting algorithm to build a integration- neural network Models for reservoir regulation.
     为了提高调度模型的准确性和神经网络的泛化能力,采用Boosting方法,进行网络集成。
  “boosting algorithm”译为未确定词的双语例句
     Intrusion Detection Based on Boosting Algorithm
     基于Boosting算法的入侵检测
短句来源
     The Consistency of the Multi-Category Classification Boosting Algorithm
     多分类Boosting算法的一致性
短句来源
     Semi-structured Text Information Extraction Based on Boosting Algorithm
     基于Boosting的半结构化信息抽取
短句来源
     Design of auto text categorization classifier based on Boosting algorithm
     基于Boosting算法的文本自动分类器设计
短句来源
     Boosting Algorithm Based Research in Face Recognition Methods
     基于Boosting算法的人脸识别方法研究
短句来源
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  boosting algorithm
In this paper, we address this problem by proposing a new regularized boosting algorithm LPnorm2-AdaBoost (LPNA).
      
Empirical studies with seven UCI datasets and one real-world application have indicated that the proposed boosting algorithm is more robust and effective than the AdaBoost.OC algorithm for multi-class learning.
      
We proved the bounds for both the empirical training error and the marginal training error of the proposed boosting algorithm.
      
In this paper, we propose a new boosting algorithm, named "MSmoothBoost", which introduces a smoothing mechanism into the boosting procedure to explicitly address the overfitting problem with AdaBoost.OC.
      
We show that the hard-core set construction of Impagliazzo (1995), which establishes the existence of distributions under which boolean functions are highly inapproximable, may be viewed as a boosting algorithm.
      
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Text categorization is defined as the task of assigning pre-defined category label to a new text.Spherical k-means algorithm is applied to obtain the category label of each text,and a classier is built based on the Boosting algorithm.This classier has following characteristics:good practicability,good extensibility and good category precision.

文本分类是指在给定分类体系下,根据文本的内容自动确定文本类型的过程。文章应用球形的k-均值算法确定每个文本的类标签,并通过Boosting算法构建分类器。构建的分类器具有以下特点:分类器的设计针对未知类标签的语料库,实用性好;分类器能随着语料库中文本的变化而增加新的类,具有很好的可扩展性;分类器基于Boosting算法,具有很好的分类精度。

Stumps, classification trees with only one split at the root node, have been shown by Schapire and Singer to be an effective method for text categorization when embedded in a boosting algorithm as its base classifiers. In their experiments, the splitting point (the partition) of each stump is decided by whether a certain term appears or not in a text document, which is too weak to obtain satisfied accuracy even after they are combined by boosting, and therefore the iteration times needed by boosting...

Stumps, classification trees with only one split at the root node, have been shown by Schapire and Singer to be an effective method for text categorization when embedded in a boosting algorithm as its base classifiers. In their experiments, the splitting point (the partition) of each stump is decided by whether a certain term appears or not in a text document, which is too weak to obtain satisfied accuracy even after they are combined by boosting, and therefore the iteration times needed by boosting is sharply increased as an indicator of low efficiency. To improve these base classifiers, an idea is proposed in this paper to decide the splitting point of each stump by all the terms of a text document. Specifically, it employs the numerical relationship between the similarities of the VSM-vector of text document and the representational VSM-vector of each class as the partition criteria of the base classifiers. Meanwhile, to further facilitate its convergence, the boosting weights assigned to sample documents are introduced to the computation of representational VSM-vectors for possible classes dynamically. Experimental results show that the algorithm is both more efficient for training and more effective than its predecessor for fulfilling text categorization tasks. This trend seems more conspicuous along with the incensement of problem scale.

为提高文本分类的精度,Schapire和Singer尝试了一个用Boosting来组合仅有一个划分的简单决策树(Stumps)的方法.其基学习器的划分是由某个特定词项是否在待分类文档中出现决定的.这样的基学习器明显太弱,造成最后组合成的Boosting分类器精度不够理想,而且需要的迭代次数很大,因而效率很低.针对这个问题,提出由文档中所有词项来决定基学习器划分以增强基学习器分类能力的方法.它把以VSM表示的文档与类代表向量之间的相似度和某特定阈值的大小关系作为基学习器划分的标准.同时,为提高算法的收敛速度,在类代表向量的计算过程中动态引入Boosting分配给各学习样本的权重.实验结果表明,这种方法提高了用Boosting组合Stump分类器进行文本分类的性能(精度和效率),而且问题规模越大,效果越明显.

A novel method for audio feature extraction and recognition is presented In this method, FBM (fractional brownian motion) based fractal dimension is defined as audio fractal feature According to Gaussian distribution characteristic of audio fractal feature, Ada boosting algorithm is used for feature reduction Then two classifiers, weighted Ada Gaussian classifier and support vector machine, are implemented respectively for audio classification Based on these two classifiers, a multiple classifier...

A novel method for audio feature extraction and recognition is presented In this method, FBM (fractional brownian motion) based fractal dimension is defined as audio fractal feature According to Gaussian distribution characteristic of audio fractal feature, Ada boosting algorithm is used for feature reduction Then two classifiers, weighted Ada Gaussian classifier and support vector machine, are implemented respectively for audio classification Based on these two classifiers, a multiple classifier model is finally constructed Experimental data shows that audio fractal feature achieves better performance than other audio features for music and speech classification

提出了一种基于分形布朗运动的音频特征提取和识别方法 这种方法使用分形布朗运动模型计算出音频例子的分形维数 ,并作为其分形特征 针对音频分形特征符合高斯分布的特点 ,使用AdaBoosting算法进行特征约减 然后分别使用Ada 加权高斯分类器和支持向量机对约减特征后的音频分类 ,并在两类分类的基础上构造多类分类的模型 实验表明 ,经过特征约减后的音频分形特征在音乐和语音的分类中都优于其他音频特征

 
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