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文章基本信息

  • 标题:A Comparative Study of Training Algorithms for Supervised Machine Learning
  • 本地全文:下载
  • 作者:Hetal Bhavsar ; Amit Ganatra
  • 期刊名称:International Journal of Soft Computing & Engineering
  • 电子版ISSN:2231-2307
  • 出版年度:2012
  • 卷号:2
  • 期号:4
  • 页码:74-81
  • 出版社:International Journal of Soft Computing & Engineering
  • 摘要:Classification in data mining has gained a lot of importance in literature and it has a great deal of application areas from medicine to astronomy, from banking to text classification.. It can be described as supervised learning algorithm as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. The classification techniques are help to learn a model from a set of training data and to classify a test data well into one of the classes. This research is related to the study of the existing classification algorithm and their comparative in terms of speed, accuracy, scalability and other issues which in turn would help other researchers in studying the existing algorithms as well as developing innovative algorithms for applications or requirements which are not available.
  • 关键词:classification; decision tree; nearest neighbour;neural network; SVM; Supervised learning
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