中文

Blindly selecting method of training samples baded data's intrinsic character for machine learning

Hits:

  • Key Words:intrinsic assembling feature; training samples; easily separable dimension; representative sample set; machine learning

  • Abstract: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 the training samples for machine learning. Firstly, we calculate each dimension's probability density estimating(PDE) to find the easily separable dimensions of the multidimensional data, then gain the smallest representative sample sets of all objects through intersecting the data of the same object of each easily separable dimensions, and get the object's 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.

  • Volume:

  • Issue:

  • Translation or Not:no


Laoshan Campus-99 Songling Road, Qingdao City, Shandong Province
Sifang Campus-No.53 Zhengzhou Road, Qingdao City, Shandong Province
Sino-German International Cooperation Zone (Sino-German Campus)-No. 3698 Tuanjie Road, West Coast New District, Qingdao City, Shandong Province
Gaomi Campus-No. 1 Xingtan West Street, Gaomi City, Shandong Province
Jinan Campus-No. 80 Wenhua East Road, Jinan City, Shandong Province ©2015 Qingdao University of Science and Technology
Administrator email: master@qust.e
Click:
  MOBILE Version

The Last Update Time:..