Artificial intelligence (AI) technology has been widely applied in various fields in recent years. Nevertheless, no systematic study has yet been conducted on the effects of AI technology on different fields. In this study, the impacts of the latest AI technology on technological innovation in different fields were analysed and quantized systematically from a patent perspective. Moreover, trends on AI technological innovation in some fields were analysed thoroughly. We conducted this study on a dataset of patents related to AI technology. Based on the patent dataset, we carried out a statistical analysis on technology fields, which we defined and classified based on international patent classification (IPC) number. Distributions of IPC in different fields were also analysed to determine the trends on AI technological innovation. The research conclusions can provide useful information to investors and enterprises, who are interested in the state of the art concerning AI technology.
Diversity among the members of classifiers is deemed to be a key point in classifier ensemble. However, there doesn't exist a widely accepted diversity measure and construct. In this paper, we propose a sample and feature double random construction of training sample variability. A support vector machine is used as the base classifier to construct the difference by distinguishing the regularization term C and the kernel function. Based on the negative correlation theory, the base classifier generalization error and disparity judgment functions are proposed, and the base classifier is integrated by ranking according to the judgment functions, which could achieve a higher accuracy rate by the support vector machine ensemble.
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