Long training times can be caused by the presence of an input vector whose length is much larger or smaller than the other input factors.
This technique aims at reducing the dimensionality of an input vector for a NN by retaining only the most informative inputs, i.e., those who have the strongest influence on the NN output.
For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM.
29 32 In the Random Forest method, when classifying a new object from an input vector, the input vector is classified by each of the trees in the forest and the tree 'votes' for that class.
This generates an input vector of 20 dimensions.
The disturbance state must be driven by an unpredictable input vector, the noise, which can only be estimated from the model error i.e. the difference between plant and model output.
Each sequence was represented by an input feature vector.
These two identical arrays are applied by the input vector, a 0, a 1, …, a n − 1, and the inversion, a ' 0, a ' 1, …, a ' n − 1, respectively, as shown in Fig. 1a.
In the above parametrization the decision boundary always contains the origin (z=0), but the more general case of an affine decision boundary ({tilde{z}in mathbb {R}^{n}: tilde{w}^{T} tilde{z} = b}) can be obtained by extending the input vector by a constant (z = (tilde {z}^{T}, 1)^{T}in mathbb {R}^{n+1}) and similarly defining an extended weight vector (w= tilde{w}^{T}, -b)^{T}).
An experiment ℰ i is defined by an experimentally feasible input vector u i (t) and a set of measurable quantities y i = (y1 i,..., y nyi i).
