Context changes detection by one-class svms
published: Feb. 25, 2007, recorded: July 2005, views: 5251
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Description
For a system that aims at taking into account the user, we need to consider that there are many different behaviors as well as many different users. Hence we need adaptative, unsupervised (or semi-supervised) learning methods. Our idea is to take advantage of wearable computers and wearable sensors (indeed their use is realistic at least for certain categories of people, such as pilots) to retrieve the current context of the user. Wearable sensors can be physiological (EMG, ECG, blood volume pressure...) or physical (accelerometers, microphone...). Contexts are depending on the application using the system and can be behaviors, affective states, combinations of these. Since this problem of context retrieval is very complex, we choose to detect changes at first place instead of labeling directly. Indeed this way we can apply unsupervised and fast methods which saves time for labeling (the labeling task is then applied only when changes are detected). Our interest lies in low level treatments and we present a non parametric change detection algorithm. This algorithm is meant to provide sequences of unlabeled contexts to be analyzed to higher level applications. Detection is made from signals given by non invasive sensors the user is wearing. Note that the methods presented here could as well be adapted to external sensors.
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In the realm of context changes detection, One-Class Support Vector Machines (SVMs) emerge as a sophisticated and efficient tool. The essence of this method lies in its ability to learn the patterns of a particular class, in this case, the 'normal' context, and subsequently detect deviations as potential context changes. This essay delves into the application of One-Class SVMs in context changes detection, exploring the underlying principles, advantages, and challenges associated with this approach. From anomaly https://bit.ly/45VRjM3 detection in cybersecurity to identifying shifts in linguistic contexts, the versatility of One-Class SVMs showcases their potential in addressing the dynamic nature of contextual variations across different domains. Understanding the nuances of this method contributes not only to the advancement of detection mechanisms but also to the broader landscape of machine learning applications.
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