Designing Early Warning System

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Details

Currency crisis is a never ending episode in the economics story. Some questions like how to prevent this crisis had been answered a long time ago. But how accurate the prediction can be? In this book, we introduce an application of machine learning in modeling an early warning system to predict currency crisis with the aim of increasing the prediction accuracy. Also, this book introduces WEKA software which is a collection of machine learning algorithms for data mining.

Autorentext

Nor Azuana Ramli is a PhD student from Malaysia. She received a BSc degree in mathematical industry from Universiti Teknologi Malaysia in 2008. Her research involves the application of machine learning system in modeling Early Warning System to predict currency crisis. This is her first book which is also a part of her ongoing research.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783659482359
    • Sprache Englisch
    • Genre Anwendungs-Software
    • Größe H220mm x B150mm x T4mm
    • Jahr 2013
    • EAN 9783659482359
    • Format Kartonierter Einband
    • ISBN 3659482358
    • Veröffentlichung 05.12.2013
    • Titel Designing Early Warning System
    • Autor Nor Azuana Ramli
    • Untertitel Prediction accuracy of currency crisis by using k-nearest neighbour method
    • Gewicht 107g
    • Herausgeber LAP LAMBERT Academic Publishing
    • Anzahl Seiten 60

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