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The classical perceptron algorithm is an elementary algorithm for solving a homogeneous linear inequality system Ax > 0, with many important applications in learning theory (e.g., [11,8]).
The filtered derivative algorithm is another elementary algorithm introduced by Basseville and Gasnier [19] in 1981, based on the gradient techniques.
To achieve our objectives in terms of detection delay, on-line analysis (only one pass over the whole data) and without any a priori knowledge about IP traffic characteristics, we choose to focus in this paper on an elementary algorithm: EWMA [18] proposed by Roberts.
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The elementary algorithms use simple and intuitive concepts.
They classified these algorithms into three main categories: the elementary algorithms, the cumulative sum algorithm, and the Bayes-type algorithms.
We provide a set of elementary algorithms to do various calculations such as distance between two descriptor image points in the feature space.
Alternatively, the protocols can be based on elementary algorithms, either on paper or computerized, which allow less freedom for the nursing staff (14– 26).
The machine he used, an I.B.M. 7090, was given twenty-six standard forms certain elementary integrals and eighteen simple algorithms.
We refer below to our elementary matching algorithm as Compart matching, because the implementation of the algorithm is embedded in the same software tool that does the compartmentization.
The machine he used, an I.B.M. 7090, was given twenty-six standard forms — certain elementary integrals — and eighteen simple algorithms.
These profiles characterize the performance of each elementary anytime algorithm as a function of run-time and input quality.
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