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Exact(38)
The training samples are audio feature vectors which are distributed in a multidimensional feature space.
There exist a myriad methods to partition our multidimensional feature space into several classification regions.
Instead, multiple variances are determined for different frequency ranges resulting in a multidimensional feature vector.
Thus, UWB data detection is formulated as a pattern classification problem in a multidimensional feature space.
The idea behind SVR is based on the computation of a linear regression in a multidimensional feature space.
An effective method for designing neural network to classify patterns in the multidimensional feature space is introduced.
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For multidimensional features, classification becomes more difficult.
All these methods use multidimensional features or combined approaches to represent the face images.
The multidimensional features obtained from QSAR are having high overlapping and it is very difficult to classify it into toxic/nontoxic groups.
For these multidimensional features, we employed a neural network as a classifier to make a decision on the presence of speech.
We chose neural networks in place of codebooks in order to have a common classifier for the multidimensional features from all categories.
More suggestions(15)
multidimensional role
multidimensional functionality
multidimensional character
multidimensional trait
multidimensional aspect
multidimensional element
multidisciplinary feature
multifunctional feature
versatile feature
multivariate feature
multilevel feature
layered feature
three dimensional feature
multidimensional features
manifold feature
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