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第698行：
第698行： − + 第706行：
第706行： − 在数据挖掘中，异常检测，也被称为异常检测，是识别那些引起怀疑的稀有项目，事件或者观察结果，它们与大多数数据有很大的不同。一般来说，这些不正常的项目代表一个问题，如银行欺诈、结构缺陷、医疗问题或文本中的错误。异常被称为异常值、新奇值、噪音、偏差和异常。+ 第714行：
第714行： − 特别是在滥用和网络入侵检测的背景下，感兴趣的对象往往不是罕见的对象，而是突发性的活动。这种模式并不符合异常值作为稀有对象的通用统计定义，而且许多异常检测方法(特别是无监督算法)将无法处理这类数据，除非它已经被适当地聚合。相反，数据聚类算法可以检测到这些模式形成的微团簇。+ 第722行：
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→Anomaly detection
稀疏词典学习是一种特征学习方法，在这种方法中，一个训练样本被表示为基函数的线性组合，并假设为稀疏矩阵。该方法具有强 NP- 困难性并且近似求解困难。一种流行的'''启发式 Heuristic'''稀疏字典学习方法是 K-SVD 算法。稀疏词典学习已经应用于以下几种情况下：在分类中，问题在于如何确定先前未见的训练样本所属的类；对于已经构建了每个类的字典，一个新的训练示例将与相应的字典最好地稀疏表示的类相关联。稀疏字典学习也被广泛应用到图像去噪的问题中。其关键思想是，一个干净的图像'''补丁 patch'''可以由图像字典稀疏地表示，但噪声不能。
稀疏词典学习是一种特征学习方法，在这种方法中，一个训练样本被表示为基函数的线性组合，并假设为稀疏矩阵。该方法具有强 NP- 困难性并且近似求解困难。一种流行的'''启发式 Heuristic'''稀疏字典学习方法是 K-SVD 算法。稀疏词典学习已经应用于以下几种情况下：在分类中，问题在于如何确定先前未见的训练样本所属的类；对于已经构建了每个类的字典，一个新的训练示例将与相应的字典最好地稀疏表示的类相关联。稀疏字典学习也被广泛应用到图像去噪的问题中。其关键思想是，一个干净的图像'''补丁 patch'''可以由图像字典稀疏地表示，但噪声不能。
==== Anomaly detection ====
==== 异常检测 Anomaly detection ====
{{Main|Anomaly detection}}
{{Main|Anomaly detection}}
In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.
In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions.
在数据挖掘中，'''异常检测 Anomaly / Outlier detection'''是指识别那些引起怀疑的稀有项目、事件或者观测结果，它们与其他大多数数据有很大的不同。一般来说，这些不正常的项目都可以反映出数据背后的一个问题，如银行欺诈、结构缺陷、医疗问题或文本中的错误。异常也被称为''异常值 Outliers''、''奇异值 Novelties''、''噪音 Noise''、''偏差 Deviations''和''异常 Exceptions''。
In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.
In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular, unsupervised algorithms) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns.
特别是在滥用和网络入侵检测的背景下，人们感兴趣的往往不是罕见的对象，而是突发性的活动。这种模式并不符合异常值作为稀有对象的通用统计学定义，而且许多异常检测方法（特别是无监督的算法）将无法处理这类数据，除非它已经被适当地聚合处理。相反地，数据聚类算法可以检测到这些模式形成的微团簇。
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set and then test the likelihood of a test instance to be generated by the model.
异常检测技术有3大类。无监督的异常检测 / 测试技术在假设数据集中的大多数实例都是正常的情况下，通过寻找似乎最不适合数据集的其余部分的实例，检测未标记的测试数据集中的异常。监督异常检测分析技术需要一个被标记为“正常”和“异常”的数据集，还需要训练一个分类器(许多其他分类分析问题的关键区别在于异常检测本身的不平衡性)。半监督异常检测技术从给定的正常训练数据集构建一个表示正常行为的模型，然后测试由该模型生成的测试实例的可能性。
异常检测技术有3大类。无监督的异常检测 / 测试技术在假设数据集中大多数实例都是正常的情况下，通过是来寻找数据集中最违和的实例，从而实现检测未被标记的测试数据集中的异常。监督式的异常检测分析技术需要一个被标记为“正常”和“异常”的数据集，还需要训练一个分类器（和许多其他分类分析问题的关键区别在于异常检测本身的不平衡性）。半监督的异常检测技术从给定的正常训练数据集构建一个表示正常行为的模型，然后测试由该模型生成的测试实例的可能性。
====Robot learning====
====Robot learning====