  ![Boxplots of $L_{\mathrm{par}}$ in (\[Lpar\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCP"}](synC0_00P0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of $L_{\mathrm{par}}$ in (\[Lpar\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCP"}](synC0_00P0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of $L_{\mathrm{par}}$ in (\[Lpar\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCP"}](synC0_00P0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of $L_{\mathrm{comp}}$ in (\[Lcomp\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCC"}](synC0_00C0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of $L_{\mathrm{comp}}$ in (\[Lcomp\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCC"}](synC0_00C0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of $L_{\mathrm{comp}}$ in (\[Lcomp\]) for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCC"}](synC0_00C0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of the classification errors for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCD"}](synC0_00D0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of the classification errors for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCD"}](synC0_00D0.pdf){width="00mm"}  \[fig:one\]  ![Boxplots of the classification errors for the dataset tilting the mixture coefficient: The compared methods are the maximum entropy approach (ME), the non-regularized estimator (NR), the proposed methods (R0; R00; R000) and its version with the cross-validation (RCV). The results with different values of the concentration parameter $w^{\ast}$ are shown in (A)-(C).[]{data-label="synCD"}](synC0_00D0.pdf){width="00mm"}  \[fig:one\]  