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A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.

A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.