In the result, the selection cutoff is informed by the classifier performance curve: one chooses the maximal acceptable classification error for the problem at hand and stops feature selection procedure as the error on current best subset (green curve in Figures 2 and 3) exceeds this threshold.
By using the above fixed point formulation, we can define the following p-step projection iterative algorithm with mixed errors for solving the problem (3.4).
This fixed point formulation allows us to construct the following p-step projection iterative algorithm with mixed errors for solving the problem (3.3).
This fixed point formulation enables us to define the following p-step projection iterative algorithm with mixed errors for solving the problem (3.1).
By using this fixed point formulation, we can construct the following p-step projection iterative algorithm with mixed errors for solving the problem (3.1).
This fixed point formulation enables us to construct the following p-step projection iterative algorithm with mixed errors for solving the problem (3.5).
In the sequel, we will develop and analyze a new class of iterative methods and construct a new random iterative algorithm with mixed errors for solving the problem (3.1).
The stability theory with explicit error estimate for the problem is still limited.
It is based on Nakao's method by using finite element approximation and its explicit error estimates for the problem.
In this section, by using the problems (5.1 - 5.4) and four Lemmas 5.2-5.5 5.2-5.5 some fixed point formulations for constructing a number of the neweperturbed p-step projection iterative algetithmsometh mixed errors fixedolving the pointems (3.1) and (3.3)-(3.5).
