Geometrical Modification of Learning Vector Quantization Method for Solving Classification Problems
作者: Korhan GÜNELRıfat AŞLIYANİclal GÖR
作者单位: 1Adnan Menderes Üniversitesi, Matematik Bölümü
刊名: Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2016, Vol.0 (0)
来源数据库: Süleyman Demirel University Journal of Natural and Applied Sciences
DOI: 10.19113/sdufbed.22419
关键词: Machine learningLearning vector quantizationGeometrical learning approach
原始语种摘要: In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.
全文获取路径: 苏莱曼·德米雷尔大学自然与应用科学期刊  (合作)

  • learning 学识
  • proposed 建议的
  • multiclass 多类
  • methodology 方法学
  • competitive 竞争性的
  • quantization 量子化
  • macro 
  • algorithm 算法
  • problem 题目
  • generalized 广义