Found 8 datasets matching "one-class-support-vector-machine".
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The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files.
Search relevance: 91.67 | Views last month: 1 -
One-class nu-Support Vector machine (SVMs) learning technique maps the input data into a much higher dimensional space and then uses a small portion of the training data (support vectors) to...
Search relevance: 69.44 | Views last month: 0 -
In this paper we propose $\nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector...
Search relevance: 68.55 | Views last month: 0 -
In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector...
Search relevance: 66.42 | Views last month: 1 -
In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector...
Search relevance: 65.82 | Views last month: 1 -
This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a subscale Solid Rocket Motor...
Search relevance: 61.26 | Views last month: 2 -
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...
Search relevance: 55.37 | Views last month: 3 -
An optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It currently use Kalman filtering...
Search relevance: 37.43 | Views last month: 0
