outliers

Outliers opens with a typically Gladwellian puzzle :

《异类》一开始就提出了一个典型的格拉德威尔之谜：

To illustrate this point , in Outliers Gladwell talks about two men with genius level intellect & Christopher Langan and Robert Oppenheimer .

为阐释这一点，在《异类》（Outliers）一书中，马尔科姆·格拉德威尔（Gladwell）谈到两个天才级别的人&克里斯托弗·兰根（ChristopherLangan）和罗伯特·奥本海默（RobertOppenheimer）。

Implementation of Network Intrusion Detection System Based on Density Outliers Mining

基于密度的异常挖掘智能网络入侵检测系统设计与实现

It introduces an outliers detection method based on random forest .

提出一种基于随机森林方法的异常样本（outliers）检测方法。

Treatment of Outliers of Test Data in Product Quality Inspection

产品质量检测中试验数据异常值的处理

Finally , we study the outliers in the model to improve the model .

最后对模型中的异常点进行探查从而对模型进行改进。

Study on Outliers Detection Algorithm in Medical Image Based on FSVM

基于FSVM的医学图像奇异点检测算法研究

New Approach of Spatial Outliers Measurement and Detection in Spatial Databases

空间数据库中离群点的度量与查找新方法

Nonlinear Data Transformation and Its Application in Clustering with Outliers

非线性数据变换及其在离群聚类中的应用

An Algorithm of Outliers Mining Based on Grid Clustering Techniques

基于网格聚类技术的离群点挖掘算法

Discovery of High Dimensional Outliers Based on Association Analysis

基于关联分析的高维空间异常点发现

Study for Outliers Based Information Diffusion with Kalman Filter

基于信息扩散的Kalman滤波抗野值研究

Therefore , new methods need be proposed to detect outliers in data streams .

因此需要研究数据流中的离群点检测方法。

An Algorithm for Clustering of Outliers Based on Key Attribute Subspace

一种基于关键域子空间的离群数据聚类算法

A BP Neural Networks Based Spatial Outliers Detecting Method

一种基于BP神经网络的空间异常探测方法

Dynamic outliers data identifying and detection based on chaotic

基于混沌的异常数据的动态识别与挖掘

The Research of the Methods of Outliers Mining

异常挖掘方法研究

Real-time Outliers Detection in Multi-Source Trajectory Data Based on Weighted Fusion

基于加权融合的多信源弹道数据实时野值检测方法

The following section first shows how to enrich outliers with additional information .

接下来的小节首先展示如何用附加信息扩展离群值。

New Approach Based on Square Neighborhood to Detect Outliers

基于方形邻域的离群点查找新方法

The inspection of outliers by ARMA model in time series

基于ARMA模型检出时间序列中的离群值

Rather , the user must specify a threshold for outliers .

实际上，用户必须指定一个阈值，以便界定离群值。

However , none of these three methods are particularly resistant to outliers .

但是，这三种方法中没有一种对于异常值有较强的抵抗力。

Outliers Improvement Based on the Strong Tracking Filter

基于强跟踪滤波器的抗飞点容错滤波

Kalman Prediction Algorithm Restraining Outliers Based on Innovation Property

基于新息特性抗野值Kalman预测算法

Mean rate methods of testing for outliers

异常数据检验的均值比方法

Outliers Mining and Application Based on Clustering

基于聚类的离群数据挖掘及应用

Application of H ∞ Filtering for Outliers Restraining in Integrated Navigation

抗野值H∞滤波在组合导航中的应用

Outliers Detection in Time Series Based on One-class Classification

基于一类分类的时间序列异常值检测

The Local Outliers Mining System of Celestial Body Spectrum Based on Constrained Concept Lattice

基于约束概念格的天体光谱局部离群数据挖掘系统