Blindly Selecting Method of Training Samples Based Datas Intrinsic Character for Machine Learning

摘要：

The supervised machine learning is the main analyzing method for the object recognition, but, when we analyze the multidimensional data using the supervised learning method, how can we get the training data from the data itself without other previous knowledge? Based on the intrinsic assembling feature of the multidimensional data, we present a method to select me training samples for machine learning. Firstly, we calculate each dimensions probability density estimating(PDE) to find the easily separable dimensions of the multidimensional data, then gain me smallest representative sample sets of all objects through intersecting the data of the same object of each easily separable dimensions, and get the objects number and the training data sources for the machine learning at the same time; secondly, train the neural network ensembles using the data selected from the representative sample sets to label the other data. Lastly, we analyzed the hyper-spectral images to detect red tide using this method, which proved this method could recognize the red tide effectively.

关键词： Intrinsic assembling feature Training samples Easily separable dimension Representative sample set Machine learning.

作者: Wencang Zhao

作者单位: College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao 266042, China

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 799-804

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）