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.
Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified...
An interesting class of irregular algorithms is tree traversal algorithms, which repeatedly traverse various trees to perform efficient computations. Tree traversal algorithms form the algorithmic kernels in an important set of applications in scientific computing, computer graphics, bioinformatics, and data mining, etc. There has been increasing interest in understanding tree traversal algorithms,...
Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
The climate model is the crucial factor for agriculture. However, the climate variables, which were strongly corrupted by noises or fluctuations, are complicated process and can not be reconstructed by a common method. In the paper, we adapt the SVM to predict it. Specifically, we incorporate the initial condition on climate variables to the training of SVM. The numerical results show the effectiveness...
Many problems in intelligent data analysis involve some forms of dimensionality reduction. The paper discusses a new supervised dimensionality reduction method where samples are accompanied with class labels. We also show that it can be easily extended to the non-linear dimensionality reduction scenarios by the kernel tricks, and then we proposes an effective orthogonal feature subspace and correlation...
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.