The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Abstract-Prediction of protein-proteininteraction sites is very important to the function of a protein and drug design. In this paper, we adequately utilize the characters of ensemble learning, which can improve the accuracy of individual classifier and generalization ability of the system, and propose a new prediction method of protein-protein interaction sites: ensemble learning method based on...
The identification of protein-protein interface residues is essential for drug design, understanding cell activity of organism. In this paper, a couple of covering algorithms are presented to predict protein-protein interaction sites by using several protein features, such as sequence profile, residue entropy and so on. These features are utilized to construct covering algorithms classifiers to identify...
A good feature extraction method can improve the performance of pattern recognition system or classification system. The contour can better to retain the original features of the object. In target recognition system , using potential energy of contour-point projection into the plane coordinate system. The method can be better to show a contour in the structural feature. In addition, it's better avoid...
Support vector machine for pattern classification is motivated by linear machines, but rely on preprocessing the data to represent in a high dimension with an appropriate nonlinear mapping, data from two categories can by separated by a hyperplane. To make certain the hyperplane, the key problem is selecting appropriate criterion and algorithm. To find out the appropriate solution vector in solution...
Classification of high-dimensional data generally requires enormous processing time. In this paper, we present a fast two-stage method of support vector machines, which includes a feature reduction algorithm and a fast multiclass method. First, principal component analysis is applied to the data for feature reduction and decorrelation, and then a feature selection method is used to further reduce...
To address the multiclass classification problem of hyperspectral data, a new method called pairwise decision tree of support vector machines (PDTSVM) is proposed. For an N -class problem, after training N(N - 1)/2 binary support vector machines (SVMs) for each pair of information class, PDTSVM only requires N - 1 binary SVMs for one classification. Based on the separability estimated by the geometric...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.