Three algorithms（the Charge Comparison Method,n-γ Model Analysis and the Centroid Algorithm）have been revised to improve their accuracy and broaden the scope of applications to real-time digital n-γ discrimination...Three algorithms（the Charge Comparison Method,n-γ Model Analysis and the Centroid Algorithm）have been revised to improve their accuracy and broaden the scope of applications to real-time digital n-γ discrimination.To evaluate the feasibility of the revised algorithms,a comparison between the improved and original versions of each is presented.To select an optimal real-time discrimination algorithm from these six algorithms（improved and original）,the figure-of-merit（FOM）,Peak-Threshold Ratio（PTR）,Error Probability（EP） and Simulation Time（ST） for each were calculated to obtain a quantitatively comprehensive assessment of their performance.The results demonstrate that the improved algorithms have a higher accuracy,with an average improvement of 10%in FOM,95%in PTR and 25%in EP,but all the STs are increased.Finally,the Adjustable Centroid Algorithm（ACA） is selected as the optimal algorithm for real-time digital n-γ discrimination.展开更多
Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In thi...Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.展开更多
We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measu...We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.展开更多
文摘Three algorithms（the Charge Comparison Method,n-γ Model Analysis and the Centroid Algorithm）have been revised to improve their accuracy and broaden the scope of applications to real-time digital n-γ discrimination.To evaluate the feasibility of the revised algorithms,a comparison between the improved and original versions of each is presented.To select an optimal real-time discrimination algorithm from these six algorithms（improved and original）,the figure-of-merit（FOM）,Peak-Threshold Ratio（PTR）,Error Probability（EP） and Simulation Time（ST） for each were calculated to obtain a quantitatively comprehensive assessment of their performance.The results demonstrate that the improved algorithms have a higher accuracy,with an average improvement of 10%in FOM,95%in PTR and 25%in EP,but all the STs are increased.Finally,the Adjustable Centroid Algorithm（ACA） is selected as the optimal algorithm for real-time digital n-γ discrimination.
基金the National Natural Science Foundation of China(No.61004088)the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)
文摘Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.
基金Li’s work was partially supported by National Medical Research Council in Singapore and AcRF R-155-000-174-114.NNSF[grant number 11371142].
文摘We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.
