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In order to achieve the helter skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN).
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SVM-S employs GMM supervectors as input feature vectors [46, 47].
SVM-T employs MLLR transformation parameters as input feature vectors [48, 49].
As a second test, we trained the SemiSVR model using the same '38 pairs' input feature encoding developed by Chen.
The input features F AB for a protein pair A– B thus became: (2) where ⊕ is the concatenation operator.
Signature descriptors were used as input features.
ML classification requires input features or dimensions.
The system adopts the acoustic signals as input features.
However, most classical regression methods take vectors as input features.
Such quality of input features is not diminished.
It predicts throughput based on multiple real-value input features.
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