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.
Haze and mist always affect the quality of vision. If an image is suffered from haze or mist, then the object is unclear and the image seems whiter than the original one. There are several haze removal algorithms that can reduce the effect of haze and mist. However, if an image is not suffered from the haze and mist, applying the haze removal algorithm may darken the image. Therefore, in computer...
Kernel learning is an important learning framework in machine learning, whose main idea is a mapping from input space to feature space induced by kernel function which yields a linear separation problem in the feature space. However, the generalization ability of kernel learning, which may lead to over-fitting of training data, has not been formally taken into consideration in previous literatures...
Nonparametric Wilcoxon regressors, which generalize the rank-based Wilcoxon approach for linear parametric regression problems to nonparametric neural networks, were recently developed aiming at improving robustness against outliers in nonlinear regression problems. It is natural to investigate if the Wilcoxon approach can also be generalized to nonparametric classification problems. Motivated by...
A sample and class incremental learning algorithm based on hyper-sphere support vector machine is proposed. For every class, hyper-sphere support vector machine is used to get the smallest hyper-sphere that contains most samples of the class, which can divide the class samples from others. In the process of incremental learning, the hyper-sphere of every new class are trained, and the history hyper-spherees...
There have been a lot of researches focusing on large-scaled automatic acquisition of subcategorization frames, and many achievements have been made for lexicon building in quite a few languages, but subcategorization annotation for individual sentences still remains in a rarely touched field. This paper proposed to annotate Chinese subcategorization as a classification task by means of sequence kernel...
In this paper, a sparse learning algorithm, probabilistic classification vector machines (PCVMs), is proposed. We analyze relevance vector machines (RVMs) for classification problems and observe that adopting the same prior for different classes may lead to unstable solutions. In order to tackle this problem, a signed and truncated Gaussian prior is adopted over every weight in PCVMs, where the sign...
In many machine learning settings, labeled samples are difficult to collect while unlabeled samples are abundant. We investigate in this paper the design of support vector machine classification algorithms learning from positive and unlabeled samples only. We first find the minimum bounding sphere that enclosed all the positive samples, and then use this minimum bounding sphere to pick out the negative...
Traditional text classifiers usually concern nothing about the producing time of samples, but many samples may involve seasonal features, which contain necessary prior information for classification. This paper firstly discovered the emporal relation between classes by means of chi-square test on a 2 * p dimensional contingency table for the data set of the mayor's complain telephone texts, and then...
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.