feature selection

Web text mining feature selection

Web文本挖掘及特征选择

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In texture synthesis and classification experiments, training a SVM and prediction ( synthesis) require high processing time. To resolve the problem, a new efficient feature selection method based on Bagging ensemble learning method is introduced to reduce the number of characteristics.

最后针对纹理合成及分类实验中训练及预测（合成）速度过慢的问题，提出了一种基于Bagging集成学习的特征选择方法，用来减少特征数量提高执行速度。

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And a no-search combined feature selection method based on KNN is proposed.

在此基础上,该文提出了一种基于KNN算法的组合式非搜索特征选择算法.

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PMU Based Feature Selection for Transient Stability Prediction

基于PMU测量数据的暂态稳定预测分类特征选择

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This thesis designs 4 main algorithms on the base of Feature Selection algorithm Framework.

在特征选择算法框架的基础上,论文先后设计了4种主要的特征选择方法.

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Feature selection is the key part in image matching based on features.

提出了基于岩石颗粒特征的图象配准算法.

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To discern positive and negative example fully, feature subset selection plays a great role in learning from examples.

特征选择是示例学习的关键，直接关系到获取的概念的优劣。

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Hybrid Mutual Information Based Feature Selection Method as Appied To Static Voltage Stability Assessment in Power Systems

基于混合互信息的特征选择方法及其在静态电压稳定评估中的应用

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After analyzing some normal evaluation functions for feature selection, a new evaluation function named the ratio of mutual information in feature selection was presented.

在分析了常用的一些特征选择的评价函数的基础上，提出了一个新的评价函数，即互信息比值。

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SVR Feature Selection Method Based on Least Square Estimate Discriminative Function

一种基于LS拟合判别函数的SVR特征选择算法

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The paper defines a new feature selection criterion, i.e.Correlation Projection Score, which can help explicitly take feature interactions into account in feature selection.

以往的特征选择方法中使用的特征或者特征集评价准则往往对属性之间的相互影响考虑较少。

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Feature selection tended to favor necessary improvements over desirable improvements.

他们的选择必须的特性,而不是可拥有的特性.

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It uses semi-supervised learning algorithm based on FCM, and for feature selection.

该方案使用的算法就是基于FCM的半监督学习算法，同时可以进行特征选择。

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Study on Feature Selection Based on Learning Theory in Power Systems

电力系统中基于学习理论的特征选择方法研究

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We first analysis the characteristic of HTML documents, then discuss the key technique of automatic text classification, including Vector Space Model, Chinese word segmentation, text feature selection, and implement a multinomial NaiVe Bayes classifier to classify Chinese Web page.

本文首先分析了Web页面的组织特点，对文本自动分类中使用到的向量空间模型、分词、特征选择等关键技术进行了深入的探讨，并实现了一个多项式朴素贝叶斯分类器对中文网页进行分类。

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During the research of text associative feature selection, we summarized two principles of feature selection such as ability of document type identification and document coverage rate, and presented the selection method of combination set of k-frequent sets.

在关联特征选择的研究中，我们总结了文档类别区分能力和文档覆盖率这两个特征选择原则，提出了k项频集的并集的选择方法。

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The Bag of Words framework based on SIFT was analysised. The support vector machine was used for feature selection point of the visual vocabulary. The experiments show better performance than the k-means clustering algorithm.

以此为基础，研究了SIFT的词袋算法框架，通过支持向量机选择视觉词汇的特征点，实验表明，性能优于k均值聚类算法。

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Feature Subset Selection for KDD

KDD中的特征选择

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In this paper, a feature selection method from the point of the characteristic of spatial data, named MEFS ( maximum entropy feature selection), is proposed.

从空间数据本身的特性出发，提出一种特征选择方法MEFS（maximum entropy feature selection）。

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Simple genetic algorithm ( SGA) has the drawbacks of premature convergence and poor local searching ability, which limit the application in feature selection.

简单遗传算法（SGA）早熟收敛和局部搜索能力弱的两大缺陷，影响和制约着它在特征选择中的高效应用。

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Many researches indicate that the existence of redundant and noisy data greatly constrain the efficiency and effective of classification. The normal solution to this problem is feature selection or feature extraction.

研究表明大部分数据集中存在的冗余和噪音数据严重影响了分类的效果，一般采取的方法是属性选择或属性提取。

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The heuristic algorithm of minimal feature subset selection is proved very efficient by theoretical analysis and experiment.

通过理论分析和实验，证明了该特征选取方法的高效性。

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Aiming at the preceding problem, this paper puts forward a feature selection method using Information Gain ( IG) and Principle Component ( Analysis)( PCA).

针对上述问题，提出了信息增益（IG）与主成分分析（PCA）相结合的特征选择方法。

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Feature selection from the keyboard or'teach and learn'from the hand-box .

通过键盘或手动操作盘上的 ‘ teachandlearn ’键来选择测量元素.

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Research on a noise-tolerant algorithm for feature subset selection

容忍噪音的特征子集选择算法研究

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PRN combined feature selection algorithm is proposed.

并提出改进的PRN组合 特征选择算法.

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The improved discrete binary quantum particle swarm optimization ( PSO) algorithm and Fisher classifier which constructed based on kernels is used to do feature selection.

再用改进的离散二进制量子粒子群算法与基于核的Fisher分类器相结合进行特征选择。

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Traditional feature selection algorithms trends to break down in high dimensional Web spaces because of the ( inherent) sparsity of the data object.

传统特征选择算法在多维Web数据中由于其数据对象自身固有的稀缺性而常常失效。

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Hydrocarbon prediction method based on SVM feature selection

一种基于SVM特征选择的油气预测方法

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Optimal Feature Subset Selection for Multi-class Issue

多类问题中最优特征子集选取的研究

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