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Both ARFI and APRI could evaluate liver fibrosis stages in chronic hepatitis C. ARFI is more accurate than the APRI index.

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Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam detection, which changes at a very brisk pace. For some problems, there may exist multiple perspectives, so called views, of each data sample. For example, in text classification, the typical view contains a large number of raw content features such as term frequency, while a second view may contain a small but highly-informative number of domain specific features. We thus propose a novel two-view transductive SVM that takes advantage of both the abundant amount of unlabeled data and their multiple representations to improve the performance of classifiers. The idea is fairly simple: train a classifier on each of the two views of both labeled and unlabeled data, and impose a global constraint that each classifier assigns the same class label to each labeled and unlabeled data. We applied our two-view transductive SVM to the WebKB course dataset, and a reallife review spam classification dataset. Experimental results show that our proposed approach performs up to 5% better than a single view learning algorithm, especially when the amount of labeled data is small. The other advantage of our two-view approach is its significantly improved stability, which is especially useful for noisy real world data.

This paper proposed the optimal degree distribution algorithm of LT codes and analyzed its drawbacks in realization. On the basis of the optimal degree distribution algorithm, a suboptimal degree distribution algorithm was put forward. Through Test, with respect to the average decoding efficiency, the best decoding efficiency and the variance of decoding efficiencies, the LT codes with suboptimal degree distribution algorithm performs better than that with robust soliton degree distribution algorithm. The conclusion of research is practically valuable in improving efficiencies of data distribution applications.

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