Supervised/semi Sample Clauses

Supervised/semi supervised learning and detection These two techniques require data for the training phase. Training data consists of several packet traces including some anomalies. Inside the packet traces, each packet will be marked in order to know whether it is part of an anomaly or not and if yes, which one. In practical terms, we will split a trace in several time slots. We will then apply our model (deltoid, Gamma- FARIMA, etc.) on each time slotted data. At the same time, we plan to mark every slot containing an anomaly by using our own knowledge of the data contained in the traces, e.g., the knowledge potentially resulting of previous analysis of the traces, or because we could have synthesized ourselves these training traces. As exposed in the previous chapter, the training data used by a supervised algorithm is composed of a pair of objects. The first element of the pair is the value to feed the learning algorithm with, here, the data processed by the model. The second element is the result, here, the presence or not of an anomaly. In the same way that the supervised algorithm can determine the threshold for the temperature and humidity that make the player go to the green to play golf (cf. Annex 1), we can determine the thresholds for the values of the models that predict that we have an anomaly.
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Related to Supervised/semi

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