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Artificial Neural Networks

In: Data Mining in Agriculture

Author

Listed:
  • Antonio Mucherino

    (University of Florida)

  • Petraq J. Papajorgji

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

In the early days of artificial intelligence (AI), artificial neural networks (ANNs) were considered a promising approach to find good learning algorithms to solve practical application problems [189]. Perhaps, a certain unjustified hype was associated to their use, since, nowadays, ANNs seem to have less appeal for researchers. In fact, they are not considered to be among the top 10 data mining techniques [237]. Moreover, publications using ANNs are found not to be backed by a sound statistical analysis [75] and that statistical evaluation of ANNs experiments is a necessity [74]. There are, however, applications in which ANNs have been successfully used. Among such applications, there are the applications in the agricultural-related areas which are discussed in Section 5.4 of this chapter. Therefore, even though they may not be so appealing for some researchers anymore, we decided to dedicate this chapter to ANNs.

Suggested Citation

  • Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Artificial Neural Networks," Springer Optimization and Its Applications, in: Data Mining in Agriculture, chapter 0, pages 107-122, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-88615-2_5
    DOI: 10.1007/978-0-387-88615-2_5
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