助手标题  
全文文献 工具书 数字 学术定义 翻译助手 学术趋势 更多
查询帮助
意见反馈
   optimal feature subset 的翻译结果: 查询用时:0.008秒
图标索引 在分类学科中查询
所有学科
自动化技术
计算机软件及计算机应用
更多类别查询

图标索引 历史查询
 

optimal feature subset
相关语句
  最优特征子集
     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
     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
     As optimal feature subset selection(OFSS) is an NP-hard Problem,it is of realistic significance to find an approximative algorithm.
     指出最优特征子集选择问题(OFSS)是个NP-Hard问题,寻找一个近似算法具有现实意义。
短句来源
更多       
  “optimal feature subset”译为未确定词的双语例句
     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
     AN ALGORITHM FOR THE OPTIMAL FEATURE SUBSET SELECTION
     一种最优特征集的选择算法
短句来源
     (2) The optimal feature subset selection is also a NP-hard one and there are many limits in previous algorithms.
     (2)选择合理有效的简明属性集,是粗糙集研究的重要内容。
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 approaches to the subject include: genetic algorithm for optimal feature subset selection; k-fold cross-validation method for error rate evaluation and BP neural network for fault classification.
     针对此问题,提出使用基于绕封模型的故障特征选择方法,它采用遗传算法对特征集寻优,样本划分法进行错误率预测估计和BP神经网络学习算法进行分类。
短句来源
更多       
  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
     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
     AN ALGORITHM FOR THE OPTIMAL FEATURE SUBSET SELECTION
     一种最优特征集的选择算法
短句来源
查询“optimal feature subset”译词为用户自定义的双语例句

    我想查看译文中含有:的双语例句
例句
为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。
  optimal feature subset
The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment.
      
The optimal feature subset is one that maximizes the value of an evaluation measure.
      
This might not lead to an optimal feature subset in terms of classification accuracy.
      
To select an optimal feature subset, we need a measure to assess cluster quality.
      
Sequential backward elimination scheme sequentially remove features from the whole feature set until an optimal feature subset is remained.
      
更多          


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(?)...

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.

Based on the example feature matrix,a heuristic search algorithm of selecting the optimal feature subset is put forward.The algorithm is tested for its efficiency.The result of its comparison with the greedy algorithm is given as well.

文中在实例特征矩阵的基础上,提出了一个最优特征集的启发式搜索算法,并对该算法的有效性进行了数据测试,给出了它与特征选择的贪心算法的比较结果.

FSS(feature subset selection) is an important problem in the fields of machine learning and pattern recognition. Minimum FSS problem has been proved NP hard. However, existing heuristic algorithms are based on the consistency of positive and negative examples set, and a more optimal feature subset is hard to be produced under the noisy data in application to real world domains. In this paper, from the degree of statistics, the effects of noisy data on FSS is analyzed firstly, and a concept of consistent...

FSS(feature subset selection) is an important problem in the fields of machine learning and pattern recognition. Minimum FSS problem has been proved NP hard. However, existing heuristic algorithms are based on the consistency of positive and negative examples set, and a more optimal feature subset is hard to be produced under the noisy data in application to real world domains. In this paper, from the degree of statistics, the effects of noisy data on FSS is analyzed firstly, and a concept of consistent feature subset which contains error rate is given. Then a heuristic algorithm——EFS (entropy based feature subset selection) based on information theoretic entropy measure and Laplace error rate is presented. It is also applied to two real world domains and is compared with GFS (greedy feature subset selection). The experimental results show that EFS can produce more representative feature subset, and can solve the noisy problem in the practical application effectively.

特征子集选择问题是机器学习和模式识别中的一个重要问题.最优特征子集选择问题已被证明是NP难题.然而,目前的特征子集选择的启发式算法是基于正反例一致的,没有考虑到实际应用中的噪音数据影响,使得选择一个较好的特征子集非常困难.首先从统计学的角度分析了噪音对特征子集选择的影响,给出含有错误率的一致特征子集概念,然后利用信息熵和拉普拉斯错误估计函数构造了特征子集选择启发式算法EFS(entropybasedfeaturesubsetselection).将该算法应用于两个实际领域的学习问题,并与GFS(greedyfeaturesubsetselection)算法进行了比较.实验结果表明,EFS选择的特征子集更具有代表性,较为有效地解决了实际应用中的噪音影响

 
<< 更多相关文摘    
图标索引 相关查询

 


 
CNKI小工具
在英文学术搜索中查有关optimal feature subset的内容
在知识搜索中查有关optimal feature subset的内容
在数字搜索中查有关optimal feature subset的内容
在概念知识元中查有关optimal feature subset的内容
在学术趋势中查有关optimal feature subset的内容
 
 

CNKI主页设CNKI翻译助手为主页 | 收藏CNKI翻译助手 | 广告服务 | 英文学术搜索
版权图标  2008 CNKI-中国知网
京ICP证040431号 互联网出版许可证 新出网证(京)字008号
北京市公安局海淀分局 备案号:110 1081725
版权图标 2008中国知网(cnki) 中国学术期刊(光盘版)电子杂志社