Table 2.

This table provides a summary of the classification methods described in the Section 2.7.

	Mapping Function	Objective Function	Min/Max Algorithm
DT	$f\left(x\right)=\left\{\begin{array}{cc}chil{d}_1\hfill & x_i\le c\hfill \\ chil{d}_2\hfill & otherwise\hfill \end{array}\right.$	Gain impurity, information gain	greedy algorithm
LDA	$f\left(x\right)=\left\{\begin{array}{cc}1\hfill & w^{T}x<c\hfill \\ -1\hfill & otherwise\hfill \end{array}\right.$	$J\left(\mathbf{w}\right)={max}_{\mathbf{w}\in {\mathbb{R}}^n}\frac{{\mathbf{w}}^T{S}_B\mathbf{w}}{{\mathbf{w}}^T{S}_W\mathbf{w}}$	Eigen value solver
SVM	$f\left(x\right)=sgn(b+{\sum }_i{y}_i{\alpha }_{i}k(x,x_i))$	${max}_{\alpha \in {\mathbb{R}}^m}\left({\sum }_i{\alpha }_i-\frac{1}{2}{\sum }_{i,j}{\alpha }_i{\alpha }_j{y}_i{y}_{j}k(x_i,x_j)\right)$	Quadratic Programming
R-SVM
MLP	$f\left(x\right)={\sum }_i{w}_i{\psi }_i(.)$	MSE, Cross entropy, Hinge	SGD, Adam
CNN	$f\left(x\right)=conv+pool+MLP$
MDRM	$f\left(\mathbf{P}\right)={arg\; min}_{j\in \{1,2,\cdots \}}{\delta }_R(\mathbf{P},{\mathbf{P}}_{{\Omega }_j})$	$J\left(P_{\Omega }\right)={arg\; min}_{{\mathbf{P}}_{\Omega }\in P\left(n\right)}{\sum }_i{\delta }_R^2({\mathbf{P}}_{\Omega },{\mathbf{P}}_i)$	Averaging approaches