Anomaly Detection : A Survey []
Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains.
We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
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& , , Inc. or its affiliates会开汽车的人是不是都会开两个轮子的机动车？_百度知道
会开汽车的人是不是都会开两个轮子的机动车？
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希望回答对你有帮助！大多数都是会开两轮子的先才会开汽车的你好！谢谢。因为从小的开始积累经验后开汽车就容易了
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太给力了，你的回答完美地解决了我的问题，非常感谢！
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这还真不是　，看个人吧！　大多数人都会两个轮子的机动车才学汽车的！如果好就给个满意吧　　谢谢
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出门在外也不愁Multiple Kernel Anomaly Detection (MKAD) Algorithm
The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files. It combines multiple kernels into a single optimization function using the One Class Support Vector Machine (OCSVM) framework. Any kernel function can be combined in the algorithm as long as it meets the Mercer conditions, however for the purposes of this code the data preformatting and kernel type is specific to the Flight Operations Quality Assurance (FOQA) data and has been integrated into the coding steps.
For this domain discrete binary switch sequences are used in the discrete kernel, and discretized continuous parameter features are used to form the continuous kernel. The OCSVM uses a training set of nominal examples (in this case flights) and evaluates test examples for anomaly detection to determine whether they are anomalous or not. After completing this analysis the algorithm reports the anomalous examples and determines whether there is a contribution from either or both continuous and discrete elements.
This software is released under the
terms and conditions of the NASA Open Source
Agreement (NOSA) Version 1.1 or later.
Multiple Kernel Anomaly Detection (MKAD) Algorithm}