本文提出了一个基于信息增益和卡方检验的属性选择算法。
This paper proposes a new future selection algorithm based on information gain and chi-square test.
通过对原有决策树学习算法的研究，提出了以分类准确度为基础的属性选择算法；
We bring forward the newly attribute chosen algorithm based on classified certain degree of condition attributes for decision attribute.
针对原有互信息属性选择存在的偏向于低频词的问题，提出了一种改进的基于互信息的军用信息属性选择算法。
Aiming at the problem of trending to the low-frequency words existing in original mutual information method, we present an improved attribute-choosing algorithm of mi.
属性选择是机器学习的核心问题之一，它关系到归纳算法的复杂性和学习性能。
The selection of attributes, which involves the complexity and performance of induction algorithms, is.
论文的改进算法以属性的频率作为选择属性的启发信息，由过滤差别矩阵得到属性的频率。
In the improved algorithm in this paper, using frequency as the heuristic information of attributes selection, getting frequency of attributes from the filtered discernibility matrix.
文章提出了多个关系整体分布方法，给出分布属性选择和处理机分配算法。
An integral distribution method of relations is proposed, which includes the algorithms of selecting distributed attribute and assigning processors.
但传统TAN的构造算法中树的根结点是随意选择的，这使得其无法精确表达属性间的依赖关系。
Since basic TAN learning algorithm choice tree structure rooted randomly, that makes it unable to express the dependence among attributes accurately.
传统的ID3决策树算法以信息增益作为属性选择的准则值，但是信息增益大的属性并不一定就是有价值的属性。
Information gain is the measurement of the attributes selection in classical decision tree algorithm-ID3, but the attributes with high information gain is not always the valuable attributes.
作者从基于分辨矩阵的粗糙集属性约简中受到启发，提出了一系列基于粗集理论的文本特征选择算法，即DB1、DB2、LDB。
The author gains insights from attribute reduction based on discernability matrix and proposes a few rough-set based text feature selection algorithms, i. e. , DB1, DB2 and LDB.
对数据离散化及属性选择度量方法作了改进，以降低计算复杂度，提高算法学习速度。
For decreasing calculation complexity and increasing speed of algorithm learning, it presented some improvement of data discretization and attributes selection measure.
该算法使分层递阶约简算法从简单的属性分层处理拓展至属性选择和属性压缩处理。
This hierarchical reduction approach extends the simple classified attributes reduction to attributes selection and attributes reduction.
本文针对单用户的网络选择提出了模糊多属性决策的两种算法。
This dissertation also puts forward to two kinds of algorithms using fuzzy multi-attribute decision making.
该算法在进行网络选择时不仅考虑到网络的负载情况，还充分考虑了发起会话的业务属性、终端的移动性以及终端在网络中所处位置的不同。
By this algorithm, the most suitable network for each session can be selected according to network load conditions, service attribute, mobility and different locations of terminal.
在模型中设计了招标选优算法，根据属性的权重，自动找出不同属性上较优的投标提供给采购方选择。
The algorithm of optimal bidding in multi-attribute auction provides the prominent bid for purchasers according to the weight of attributes.
在模型中设计了招标选优算法，根据属性的权重，自动找出不同属性上较优的投标提供给采购方选择。
The algorithm of optimal bidding in multi-attribute auction provides the prominent bid for purchasers according to the weight of attributes.
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