Gao Wanlin

Directory Services and Data Sharing for Distributed Agricultural Information Resources

Abstract The agricultural information resources is an important data assets, how to rationally plan, share and use of distributed storage of agricultural information resources, efficiently develop agricultural information resources, is the important issue of agricultural information resources needed to resolve. Based on the fully research on the current status of agricultural information systems, management processes and practices based on the user, using a distributed technical architecture, integration of J2EE framework, metadata model and directory services technology and other advanced means to build a distributed directory of agricultural information resources and data-sharing platform, the working principle of the platform and key technologies are analyzed, and from the application model, the overall structure, functions, data architecture, deployment options such as distributed agricultural information resources directory and information sharing platform was designed, without changing the data stru...

Chinese Web Content Extraction Based on Naïve Bayes Model

As the web content extraction becomes more and more difficult, this paper proposes a method that using Naive Bayes Model to train the block attributes eigenvalues of web page. Firstly, this method denoising the web page, represents it as a DOM tree and divides web page into blocks, then uses Naive Bayes Model to get the probability value of the statistical feature about web blocks. At last, it extracts theme blocks to compose content of web page. The test shows that the algorithm could extract content of web page accurately. The average accuracy has reached up to 96.2%.The method has been adopted to extract content for the off-portal search of Hunan Farmer Training Website, and the efficiency is well.

Uncertainty support vector method for ordinal regression

Ordinal regression is complementary to the standard machine learning tasks of classification and metric regression which goal is to predict variables of ordinal scale. However, every input must be exactly assigned to one of these classes without any uncertainty in standard ordinal regression models. Based on structural risk minimization (SRM) principle, a new support vector learning technique for ordinal regression is proposed, which is able to deal with training data with uncertainty. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, two algorithms have been derived. This technique extends the application horizon of ordinal regression greatly. Moreover, the problem about early warning of food security in China is solved by our algorithm.