An 11-year-old boy had a history of easy bruising and poorly healing wounds since infancy and severe, early-onset periodontitis. He also exhibited mild hypermobility of the small joints of the hands, long limbs with s...An 11-year-old boy had a history of easy bruising and poorly healing wounds since infancy and severe, early-onset periodontitis. He also exhibited mild hypermobility of the small joints of the hands, long limbs with striking arachnodactyly, and a triangular face with delicate features. Analysis of type I and type III collagens revealed no abnormalities. These findings were consistent with a diagnosis of Ehlers-Danlos syndrome typeVIII (EDS-VIII), an autosomal dominant connective tissue disorder that was recently mapped to chromosome 12q13. We draw attention to the clinical features that typify EDS-VIII, including extensive pretibial bruising, a marfanoid body habitus, and characteristic facies, as well as childhood onset of progressive periodontal disease.展开更多
This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients o...This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method:Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM.展开更多
文摘An 11-year-old boy had a history of easy bruising and poorly healing wounds since infancy and severe, early-onset periodontitis. He also exhibited mild hypermobility of the small joints of the hands, long limbs with striking arachnodactyly, and a triangular face with delicate features. Analysis of type I and type III collagens revealed no abnormalities. These findings were consistent with a diagnosis of Ehlers-Danlos syndrome typeVIII (EDS-VIII), an autosomal dominant connective tissue disorder that was recently mapped to chromosome 12q13. We draw attention to the clinical features that typify EDS-VIII, including extensive pretibial bruising, a marfanoid body habitus, and characteristic facies, as well as childhood onset of progressive periodontal disease.
基金Supported by the National Natural Science Foundation of China (No.60175020) the National High Tech Development '863' Program of China (No.2002AA117010-09).
文摘This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method:Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM.
