Batch processing mode is widely used in the training process of human motiun recognition. After training, the motion elassitier usually remains invariable. However, if the classifier is to be expanded, all historical ...Batch processing mode is widely used in the training process of human motiun recognition. After training, the motion elassitier usually remains invariable. However, if the classifier is to be expanded, all historical data must be gathered for retraining. This consumes a huge amount of storage space, and the new training process will be more complicated. In this paper, we use an incremental learning method to model the motion classifier. A weighted decision tree is proposed to help illustrate the process, and the probability sampling method is also used. The resuhs show that with continuous learning, the motion classifier is more precise. The average classification precision for the weighted decision tree was 88.43% in a typical test. Incremental learning consumes much less time than the batch processing mode when the input training data comes continuously.展开更多
China is abundant in unconventional gas, and stunning growth of gas reserves and production therefore can be expected in foreseeable future because of the exploration and development of the gas. For the next 5 to 10 y...China is abundant in unconventional gas, and stunning growth of gas reserves and production therefore can be expected in foreseeable future because of the exploration and development of the gas. For the next 5 to 10 years, conventional gas will still dominate energy market but in a long run, its influence will weaken gradually as unconventional gas kicks in and expands. Though the exploration and development of unconventional gas in China is still at a very early stage, three Chinese oil majors have already assessed the resources, including tight gas, coal-bed methane, shale gas and gas hydrate.展开更多
基金partly supported by the National Natural Science Foundation of China under Grant 61573242the Projects from Science and Technology Commission of Shanghai Municipality under Grant No.13511501302,No.14511100300,and No.15511105100+1 种基金Shanghai Pujiang Program under Grant No.14PJ1405000ZTE Industry-Academia-Research Cooperation Funds
文摘Batch processing mode is widely used in the training process of human motiun recognition. After training, the motion elassitier usually remains invariable. However, if the classifier is to be expanded, all historical data must be gathered for retraining. This consumes a huge amount of storage space, and the new training process will be more complicated. In this paper, we use an incremental learning method to model the motion classifier. A weighted decision tree is proposed to help illustrate the process, and the probability sampling method is also used. The resuhs show that with continuous learning, the motion classifier is more precise. The average classification precision for the weighted decision tree was 88.43% in a typical test. Incremental learning consumes much less time than the batch processing mode when the input training data comes continuously.
文摘China is abundant in unconventional gas, and stunning growth of gas reserves and production therefore can be expected in foreseeable future because of the exploration and development of the gas. For the next 5 to 10 years, conventional gas will still dominate energy market but in a long run, its influence will weaken gradually as unconventional gas kicks in and expands. Though the exploration and development of unconventional gas in China is still at a very early stage, three Chinese oil majors have already assessed the resources, including tight gas, coal-bed methane, shale gas and gas hydrate.
