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The machine learning correction was constructed using a step-wise procedure: descriptor selections, data division, regression and validation.
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In this procedure, descriptors that generated calculating errors or gave almost the same values for more than 90% of drugs were removed.
The complete procedure of descriptor selection is summarized in Figure 1.
Evaluation procedures consider descriptors such as deflection amplitude (Yb) and far-field vibration (fymax), which cannot relate endurance limits and wire loads.
During the procedure, both the descriptor and the number of nearest neighbors were optimized.
Firstly, a design procedure for fuzzy descriptor observer is proposed to estimate the states of the fuzzy system and unknown sensor faults.
It should be noted at this point that due to this simplification procedure the N M descriptor for molecules A and B becomes 8 5.
Following the same VT quantization procedure, the local descriptors {y j } in d m are mapped to a high-dimensional sparse Bag-of-Words (BoW) histogram (mathbf {H}_{d}=[h_{1}^{d},ldots,h_{C}^{d}]).
First, stepwise multiple liner regression was employed as a descriptor selection procedure.
This poses a challenging problem for the design of the descriptor calculation procedure.
A graphical representation is shown in Fig. 3 a and defined as follows: ,text{LBP}_{P,R}(x_{c})=sumlimits_{p=0}^{P-1} u(x_{p}-x_{c})2^{p} (1) Fig. 3 LBP descriptor: labeling procedure (a) and spatial histogram concatenation (b).
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