Incremental and adaptive learning for online monitoring of embedded software

Abstract : A lot of useful hidden information is included among everyday data, which often can't be extracted manually. Therefore, machine learning is used to get knowledge by generalizing examples. When all data is already stored, a common approach used is batch learning, but when learning comes to deal with huge amounts of data, arriving at different instances of time in sets of examples, incremental learning is the best solution. To improve the performances of supervised incremental learning algorithms specific examples are stored and used for future learning steps. In this paper is described an incremental learning framework that generates classification predictions, a partial memory approach (specific border examples stored), for the "Manage YourSelf" project, that deals with the diagnosis and monitoring of embedded platforms. This approach extends the AQ21 algorithm to solve a specific learning task. It was chosen because it is one of the latest developed and it has many useful features. The incremental solution is proposed because we are dealing with a data flow and partial-memory approaches were searched because here is no need to re-examine all the instances at every learning step. Furthermore, those notably decrease memory requirements and learning times.
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Submitted on : Friday, August 24, 2012 - 11:03:24 AM
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  • HAL Id : dumas-00725171, version 1

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Monica Loredana Angheloiu. Incremental and adaptive learning for online monitoring of embedded software. Machine Learning [cs.LG]. 2012. ⟨dumas-00725171⟩

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