Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
本文利用中尺度模式WRF V4.0.2(Weather Research and Forecasting Model,Version 4.0.2)对浙江省两次梅雨锋暴雨过程进行数值模拟,分别选用WSM6和Thompson云微物理方案、YSU和MYJ边界层方案、以及11种对流参数化方案进行试验,探究不同...本文利用中尺度模式WRF V4.0.2(Weather Research and Forecasting Model,Version 4.0.2)对浙江省两次梅雨锋暴雨过程进行数值模拟,分别选用WSM6和Thompson云微物理方案、YSU和MYJ边界层方案、以及11种对流参数化方案进行试验,探究不同积云对流参数化方案对梅雨锋暴雨的1 km高分辨率预报的影响,结果表明:(1)在对各试验的降水预报评估过程中,使用传统点对点方法和邻域法都能客观表现出各试验的预报水平,而邻域检验法能更客观地评估模式对小范围强降水的预报水平。(2)三类积云对流方案(包括:无积云对流方案、传统积云对流方案和尺度自适应积云对流方案)都能较好地模拟出小雨降水的发生情况,但随着降水强度增强至暴雨、大暴雨量级时,尺度自适应的积云对流方案对降水的预报结果有明显改善。(3)在不同微物理和边界层组合方案下,尺度自适应积云对流方案的模拟结果差异更显著,而传统积云对流方案的模拟结果的效果差异不明显。(4)在1~10 km的“灰色区域”范围内,当网格分辨率逐渐提高到1 km时,尺度自适应积云对流方案较传统积云对流方案对模式的预报结果有明显的改善。本研究的结果在一定程度上可为高精度业务预报工作中对尺度自适应积云对流参数化方案的应用提供参考。展开更多
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
文摘本文利用中尺度模式WRF V4.0.2(Weather Research and Forecasting Model,Version 4.0.2)对浙江省两次梅雨锋暴雨过程进行数值模拟,分别选用WSM6和Thompson云微物理方案、YSU和MYJ边界层方案、以及11种对流参数化方案进行试验,探究不同积云对流参数化方案对梅雨锋暴雨的1 km高分辨率预报的影响,结果表明:(1)在对各试验的降水预报评估过程中,使用传统点对点方法和邻域法都能客观表现出各试验的预报水平,而邻域检验法能更客观地评估模式对小范围强降水的预报水平。(2)三类积云对流方案(包括:无积云对流方案、传统积云对流方案和尺度自适应积云对流方案)都能较好地模拟出小雨降水的发生情况,但随着降水强度增强至暴雨、大暴雨量级时,尺度自适应的积云对流方案对降水的预报结果有明显改善。(3)在不同微物理和边界层组合方案下,尺度自适应积云对流方案的模拟结果差异更显著,而传统积云对流方案的模拟结果的效果差异不明显。(4)在1~10 km的“灰色区域”范围内,当网格分辨率逐渐提高到1 km时,尺度自适应积云对流方案较传统积云对流方案对模式的预报结果有明显的改善。本研究的结果在一定程度上可为高精度业务预报工作中对尺度自适应积云对流参数化方案的应用提供参考。
