决策边界

首先利用k均值算法将未知数据划分成某个数量的子集，然后对新数据进行支持向量机训练得到决策边界与支持矢量，最后对无标识数据进行分类。

The new algorithm is to firstly divide unlabeled data into many subsets with a new label by k-means clustering , then train the SVMs using the new data set to get decision boundary and support vectors , at last use the SVMs classifier to classify the unlabeled data .

进一步结合均值向量的差向量构建扩展决策边界特征矩阵（Ex-KDBFM）。

Furthermore the difference vector of the mean support vectors is combined to construct the Extended Kernelized Decision Boundary Feature Matrix ( Ex-KDBFM ) .

为了更有效地优化前向神经网络的求解能力，提出了一种新的综合的转换函数，将多层感知机和RBF神经网络更有机地结合起来，以产生灵活的决策边界。

In order to effectively optimizing the solution of feed-forward neural network , a new general transfer function is proposed that effectively unifies the inputs of multilayer perception and radial basis function to provide flexible decision border .

提出了获取决策边界的形式化方法和采样法。

This dissertation proposes two methods for obtaining decision boundaries , the formal method and the sampling method .

在此理论框架下，划分目标、决策边界形式和划分方法是分类器的三要素。

In this new theoretical framework , the dividing objective , the decision boundary form and the dividing methode are three elements of a classifier .

传统的模式分类方法一般需要利用两类样本（或多类样本），通过两类样本共同确定了决策边界。

In conventional pattern classification problem , data from two classes ( or multiple classes ) is needed , the decision boundary is supported by examples from both sides .

该方法应用小波多分辨率分析法提取信号的故障特征值，依据有限训练样本构造其多元高斯概率分布，计算出非线性决策边界，得到未知样本逼近贝叶斯准则的最优分类判定。

Fault feature vectors are extracted via multi-resolution analysis . The multivariate probability distribution of the feature vectors is constructed according to the calculation of nonlinear decision boundaries . Our system achieves classification approach the Bayes optimal .

本质上，神经网络学习过程是通过迭代逐步找到决策边界的过程，只有在决策边界附近的小部分样本才会最终对较优边界的确定起着重要作用。

In essence , the BP learning process is to find the decision boundaries by means of iterations , and only a small part of samples near the boundaries have a relatively important role to the optimal boundaries .

近邻分类可以生成任意形状的决策边界，其决策边界具有很高的可变性，近邻个数的确定直接影响到近邻分类器决策边界的形成。

Nearest neighbor classification can produce arbitrarily shaped decision boundaries and the decision boundaries of nearest neighbor classifiers also have high variability . The decision boundaries of nearest neighbor classifiers are directly affected by the choice of the number of the nearest neighbors .

针对目前主流算法无法对变动的数据集进行有效检测，文章提出了一种新的应用于异常检测的水平集算法，它可以将决策边界曲线的运动转化为偏微分方程的数值求解问题。

As the current algorithms can not effectively detect the changes of the dataset , the paper proposes a new method named level set based novelty detection method to apply to anomaly detection . It can change the movement of the decision boundary into the partial differential equations .

股份公司兼并财务决策的边界条件分析

Analyzing the financial boundaries for merger decision

提出了一种改进的子空间截断牛顿法，用于求解惩罚函数法迭代过程中的仅带有决策变量边界约束的子优化问题。

An improved subspace truncated-Newton algorithm is given for the bound constrained optimization problem during the iterations of penalty function method .

模型的应用应在明确政府应急决策系统边界的基础之上，选择合适的风险分析方法和准确的数据来源；并结合案例进行了演算。

To use the model should base on clarifying the boundaries of government emergency decision-making system , choosing appropriate risk analysis method and accurate data source .

概率准则下的投资决策与有效边界间的关系

The Relationship between the Investment Decision-making with Probability Criterion and the Effective Frontier

所以，矿石边界品位的确定是矿产业中的一个极为关键的决策问题，边界品位的优化是当今世界矿业界的重大科研课题之一。

To define the cutoff grade is a key decision-making , in mining production .

证券投资决策的自由边界问题

Free Boundary Problem in Portfolio Investment Decision

企业组织反映了企业内部的分工与协作、部门划分与职责范围、决策权限与组织边界的构成体系，它的先进性与合理性直接影响到企业经营活动的效率和竞争能力的强弱。

Enterprise organization reflects division and cooperation , departments ' partition and duty scope , decision-making extent of authority and composing system of organization border in internal enterprise , and its advance and rationality have direct impact to business operations efficiency and competitiveness .

在划分视角下分类模型可被看作将数据空间划分成若干决策区域的一组决策边界，分类器的训练过程可被看作划分数据空间获得决策边界的过程。

From the dividing respective , a classifier model can be regarded as a group of decision boundaries dividing the data space into several decision regions , and the training process of a classifier can be regarded as dividing the data space to obtain decision boundaries .

此外，通过对不相容决策表的正区域的决策值和边界域对原决策表进行分解，得到了一种分布式增量属性约简模型。

A distributed model of incremental attribute reduction is also presented by decomposing values of decision attribute of positive region and boundary region in non-tolerant decision table .