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K- Nearest Neighbor Algorithm For Instance Based Learning

Author

Listed:
  • CRISTINA OFELIA STANCIU

    (”TIBISCUS” UNIVERSITY OF TIMIŞOARA, FACULTY OF ECONOMIC SCIENCE)

Abstract

Instance Based Learning (IBL) results in classifying a new instance by examining and comparing it to the rest of the instances in the dataset. An example of this type of learning is the K-Nearest Neighbor algorithm which is based on examining an average Euclidian distance of the nearest k neighbors' parameters given a certain situation.

Suggested Citation

  • Cristina Ofelia Stanciu, 2012. "K- Nearest Neighbor Algorithm For Instance Based Learning," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 134-138, November.
    • Handle: RePEc:tdt:annals:v:xviii/supplement:y:2012:p:134-138
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    More about this item

    Keywords

    kowledge; Instance Based Learning; algorithm; K-NN;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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