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optimal feature
相关语句
  最优特征
     OFFSS (Optimal Fuzzy-Valued Feature Subset Selection) is an optimal feature subset selection based on fuzzy-valued extension matrix.
     最优模糊特征子集选取OFFSS(Optimal Fuzzy-Valued Feature Subset Selection)是一种用模糊扩张矩阵进行最优特征子集选择的方法。
短句来源
     OFFSS (Optimal Fuzzy-valued Feature Subset Selection) is a new fuzzy-valued feature selection method that selects an optimal feature subset from the feature space by considering both the overall overlapping degree between two classes of examples.
     OFFSS (Optimal Fuzzy-valued Feature Subset Selection)是一种新的模糊值特征选取的方法,是基于两类事例集合的重叠程度来选取特征空间中最优特征子集。
短句来源
     Combined Subspace Based Optimal Feature Extraction and Face Recognition
     基于组合子空间的最优特征抽取及人脸识别
短句来源
     Optimal Feature Subset Selection Using Genetic Algorithms
     最优特征子集的遗传算法求解
短句来源
     Optimal Feature Subset Selection of Decision Tables
     决策表最优特征子集的选择——基于粗集理论的启发式算法
短句来源
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  “optimal feature”译为未确定词的双语例句
     New kernel generalized optimal feature extraction method
     一种新的核广义鉴别特征抽取方法
短句来源
     B&B algorithm constructs a search tree, and then searches for the optimal feature subset in the tree.
     用B&B算法构造一棵搜索树,在树中搜索最优的特征子集。
短句来源
     MATLAB codes are programmed to demonstrate the effectiveness of heuristic search algorithm in selecting the optimal feature subset.
     最后用MATLAB编程验证了启发式搜索算法特征选择的有效性。
短句来源
     The Apcication of K-L Transformation on the Optimal Feature Descriptions of Debris
     K-L变换在磨粒特征参数优化中的应用
短句来源
     The program of MATLAB proves the availability of heuristic search algorithm for selecting the optimal feature subset.
     最后用MATLAB编程验证启发式搜索算法特征选择的有效性。
短句来源
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  相似匹配句对
     Feature:
     本文特色:
短句来源
     Research on Optimal Algorithms of Feature Selection
     特征选择的优化算法研究
短句来源
     The Apcication of K-L Transformation on the Optimal Feature Descriptions of Debris
     K-L变换在磨粒特征参数优化中的应用
短句来源
     DATA FEATURE
     数据信息
短句来源
     Optimal temperature for D.
     各种光强下的暗呼吸速率均随温度升高而增大。
短句来源
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  optimal feature
The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment.
      
On the other hand, the optimal feature subsets can be obtained by using the wrapper approach, but it is not easy to use because of the complexity of time and space.
      
It was shown that power spectral density is not an optimal feature, and represents only a particular case of the generalised approach.
      
Stock market prediction using artificial neural networks with optimal feature transformation
      
A novel, efficient near-optimal feature selection algorithm which we callratchet search is also presented.
      
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In this paper, the problem of feature selection is converted into the optimal pathsearching problem in a weighted directional graph. Then by means of the so called informed Best First (BF) search strategy for problem solving in AI., Algo(?)ithms GBFF and TBFF are proposed to search the optimal path, i.e., the optimal feature subset. These algorithms guarantee optimality of the selected subset without exhaustive search. In compararison with the well known Branch and Bound(B & B) algorithm, it b(?) been...

In this paper, the problem of feature selection is converted into the optimal pathsearching problem in a weighted directional graph. Then by means of the so called informed Best First (BF) search strategy for problem solving in AI., Algo(?)ithms GBFF and TBFF are proposed to search the optimal path, i.e., the optimal feature subset. These algorithms guarantee optimality of the selected subset without exhaustive search. In compararison with the well known Branch and Bound(B & B) algorithm, it b(?) been shown that the number of the expanded modes by TBFF is less (even much less) than that by B & B; In other words, TBFF is superior to B & B.

本文将模式识别中的特征选择问题转化为有向图上最佳路径搜索问题,并应用AI中的Best First(简记BF)策略搜索最佳路径,提出了特征选择GBFF和TBFF算法,证明了用它们可不穷举而一定找到最佳子集,同目前被认为最好的全局最佳算法——B&B相比,TBFF搜索的特征子集数目优于B&B.

One efficient inference method for designing optimal axial structure of gear transmission system with proper knowledge representative models is presented in this paper. Since heuristic function is used, the multiple design spaces with constrained functions have been united under one optimal search strategy, which successfully embodies the repeated and optimal feature in design. In terms of the method, the inference engine has been built in PROLOG, and proved to be highly effective in practice.

本文通过对变速齿轮传动系统轴向结构布局设计规律的研究,提出了一种适合于该类问题的知识表达方式,将改进后的带有启发函数的最佳优先搜索方法引入到该问题的求解中,使得含有多重搜索空间以及带有一定约束的设计问题统一在同一搜索策略下。根据本文提出的方法,用PROLOG语言建造的推理机,通过对实际问题的考证,证明该方法在解决传动结构自动设计上是行之有效的,并且在推理机制上成功地体现了设计过程中反复设计与优化设计的功能特征.

This paper proved that the multi-layered feed-forward neural network with linear output nodes can be used as an optimal feature extractor.It is also proved that the output function of a classifier network is a least-meansquare approximation to the Bayes decision function.For a threelayered network with linear output nodes,arbitrary approximation precision can ha obtained if the network has enough hidden nodes.On the basis of these conclusions,a combined netwrok with the functions of both feature...

This paper proved that the multi-layered feed-forward neural network with linear output nodes can be used as an optimal feature extractor.It is also proved that the output function of a classifier network is a least-meansquare approximation to the Bayes decision function.For a threelayered network with linear output nodes,arbitrary approximation precision can ha obtained if the network has enough hidden nodes.On the basis of these conclusions,a combined netwrok with the functions of both feature extraction and decision is proposed.The result shown that this combined network has better properties than that of a single network.

本文从理论上证明了具有线性输出单元的多层前馈网络能用作最优特征提取器.同时还证明了多层前馈网络分类器的输出函数是最小均方误差意义下对Bayes决策函数的逼近,对于具有线性输出单元的三层前馈网络,当隐层单元数足够多时,这种逼近能达到任意精度,在此基础上,我们提出了一个综合了特征提取网络和分类器网络的组合神经网络模型,其性能好于单个的三层前馈网络.

 
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