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Then, the detection of received signal can be formulated to determining a separating hyper-plane which divides the two-group pattern objects in a multidimensional features space, under the minimum classification errors [42].
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The training samples are audio feature vectors which are distributed in a multidimensional feature space.
Thus, UWB data detection is formulated as a pattern classification problem in a multidimensional feature space.
There exist a myriad methods to partition our multidimensional feature space into several classification regions.
An effective method for designing neural network to classify patterns in the multidimensional feature space is introduced.
The idea behind SVR is based on the computation of a linear regression in a multidimensional feature space.
Images are mapped to a multidimensional feature space, where images belonging a semantic are clustered and indexed to acquire its efficient representation.
In the curve simplification method, each frame of the video sequence can be treated as a point in multidimensional feature space.
In this technique, the input data, x, are mapped from original space into a multidimensional feature space using a kernel function and then, a linear fitting function in this space is computed.
Subsequently, this established multidimensional feature space is compressed to a two-dimension plane by the optimal features combination technique, and UWB signal detection is consequently formulated to assign these pattern points into two classes at the minimum errors criterion.
Let ({mathcal{X}}={mathbb{R}}^d) be the multidimensional feature space under investigation, e.g. medical image data, and let ({mathcal{Y}}={1,ldots,c}) be the set of classes that represents the possible values of the variable of interest.
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