• M. N. MurtyEmail author
  • Anirban Biswas
Part of the SpringerBriefs in Intelligent Systems book series (BRIEFSINSY)


Representation is important in machine-based pattern recognition, AI, and machine learning. We need to represent states and state transitions appropriately in AI-based problem-solving. Similarly, in clustering and classification, we need to represent the data points, clusters, and classes.


Representation Classification Clustering AI 


  1. 1.
    Zipf GK (1949) Human behavior and the principle of least effort. Addison-WesleyGoogle Scholar
  2. 2.
    Murty MN, Devi VS (2015) Introduction to Pattern recognition and machine learning. IISc PressGoogle Scholar
  3. 3.
    Fukunaga K (2013) Introduction to statistical pattern recognition. Academic PressGoogle Scholar
  4. 4.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley InterscienceGoogle Scholar
  5. 5.
    Francois D, Wertz V, Verleysen M (2007) The concentration of fractional distances. IEEE Trans Knowl Data Eng 19(7):873–886CrossRefGoogle Scholar
  6. 6.
    Andoni A, Indyk P (2008) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun ACM 51(1):117–122CrossRefGoogle Scholar
  7. 7.
    Murty MN, Raghava R (2016) Support vector machines and perceptrons. Springer briefs in computer science, Springer, ChamCrossRefGoogle Scholar
  8. 8.
    Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11:63–91CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBengaluruIndia

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