Click:

The Last Update Time:--

Guobin Zhu
+
Recommended Ph.D.Supervisor
Recommended MA Supervisor
Personal Information
  • Supervisor of Doctorate Candidates
  • Supervisor of Master's Candidates
  • Name (Pinyin):Zhu Guobin
  • E-Mail:
  • Education Level:研究生毕业
  • Business Address:Room 1104, School of Cyber Science and Engineering, Wuhan University
  • Gender:Male
  • Contact Information:Tel. +86-27-68778916 Mobile: +86-18040500277 http://rsgis.whu.edu.cn/index.php?m=content&c=index&a=show&catid=127&id=6311
  • Academic Titles:Director of Dept. of Spatial Information & Digital Technology
  • Alma Mater:Ben-Gurion University of the Negev
  • Teacher College:School of Remote Sensing and Information Engineering
  • Discipline: Photogrammetry and Remote Sensing
  • Honors and Titles
      2018  elected:Honorary Title of Hubei Provincial Industrial Professors
      2017  elected:Honorary Title of Innovation & Creative Talent of Yangzhou City, Jiangsu Province
      2016  elected:Honorary Title of Outstanding Communist Party Member of Wuhan University
      2016  elected:Honorary Title of Progressive Individual for Major Evaluation at Wuhan University
      2015  elected:湖北省优秀学士论文指导教师
      2015  elected:Honorary Title of Outstanding Navigator for Students of Wuhan University
      2014  elected:Honorary Title of the Yellow Crane Talent of Wuhan City
      2013  elected:Honorary Title of Representatives of the CPC Congress of Wuhan University
      2013  elected:Honorary Title of Progressive Individual for Teaching and Educating at Wuhan University
Other Contact Information
  • ZipCode:
  • PostalAddress:
  • OfficePhone:
  • Telephone:
  • email:
Student Information
Current position: Home   >   Student Information

陈婷婷

  • Date:2018-12-03
  • Hits:

  • Duration of Study: 

    2016-2018

  • Major: 

    Software Engineering

  • Research Focus: 

    金融信息工程

  • Personal Profile: 


    在对多种传统机器学习算法进行分析后，提出了基于Stacking模型融合的情感极性分类算法，将多个强分类器进行融合构造一个表现更优的模型。本文采用两层Stacking模型，第一层使用训练样本数据训练多个不同质的分类器，使用5-fold交叉验证的方式，把第一层的输出结果矩阵（5-fold输出的预测结果矩阵）作为输入数据训练第二层的模型。所有测试数据同样使用第一层的多个分类器进行预测，得到测试数据的预测结果矩阵。使用第一层的输出结果矩阵训练第二层分类模型，并进行预测得到最终分类结果。通过与传统单分类器算法和集成算法相比，本文方法的学习效果得到提升。

  • Education Level: 

    研究生毕业

  • Degree: 

    硕士

  • Current Status: 

    离校

  • Graduation Thesis Title: 

    《基于机器学习的中文情感极性分类研究》

  • Student ID: 

    2016282160033

  • Date of Registration: 

    2016-09-01

  • Date of Graduation: 

    2018-06-30

Copyright @ 2017 Wuhan University