Paperback, [PU: LAP Lambert Academic Publishing], In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-… Mehr…
Paperback, [PU: LAP Lambert Academic Publishing], In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-adjusted (Sterling ratio and Sharp ratio) trading strategies are considered. Constructed optimal trading strategies can be used as training dataset for the AI application. In the next part of the book one particular type of Machine Learning - finding optimal linear separators - is considered, and combinatorial deterministic algorithm for computing minimum linear separator set in 2 dimensions is given. In the last part of the book presented efficient algorithms for preventing overfitting. Shape constrained regression is an accepted methodology to deal with overfitting. Algorithms for nonparametric shape constrained regression in the form of isotonic and unimodal regressions are given., Business & Management<
Victor Boyarshinov: Machine Learning In Computational Finance: Practical algorithms for building artificial intelligence applications - gebrauchtes Buch
2012-04-09. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be re-issue. May be ex-library. Shippin… Mehr…
2012-04-09. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be re-issue. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!, 2012-04-09, 2.5<
Paperback, [PU: LAP Lambert Academic Publishing], In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-… Mehr…
Paperback, [PU: LAP Lambert Academic Publishing], In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-adjusted (Sterling ratio and Sharp ratio) trading strategies are considered. Constructed optimal trading strategies can be used as training dataset for the AI application. In the next part of the book one particular type of Machine Learning - finding optimal linear separators - is considered, and combinatorial deterministic algorithm for computing minimum linear separator set in 2 dimensions is given. In the last part of the book presented efficient algorithms for preventing overfitting. Shape constrained regression is an accepted methodology to deal with overfitting. Algorithms for nonparametric shape constrained regression in the form of isotonic and unimodal regressions are given., Business & Management<
2012-04-09. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be re-issue. May be ex-library. Shippin… Mehr…
2012-04-09. Good. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. May not contain Access Codes or Supplements. May be re-issue. May be ex-library. Shipping & Handling by region. Buy with confidence, excellent customer service!, 2012-04-09, 2.5<
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In the first part of the book practical algorithms for building optimal trading strategies are constructed. Both non-restricted and risk-adjusted (Sterling ratio and Sharp ratio) trading strategies are considered. Constructed optimal trading strategies can be used as training dataset for the AI application. In the next part of the book one particular type of Machine Learning - finding optimal linear separators - is considered, and combinatorial deterministic algorithm for computing minimum linear separator set in 2 dimensions is given. In the last part of the book presented efficient algorithms for preventing overfitting. Shape constrained regression is an accepted methodology to deal with overfitting. Algorithms for nonparametric shape constrained regression in the form of isotonic and unimodal regressions are given.
Detailangaben zum Buch - Machine Learning In Computational Finance: Practical algorithms for building artificial intelligence applications
EAN (ISBN-13): 9783659118890 ISBN (ISBN-10): 3659118893 Gebundene Ausgabe Taschenbuch Erscheinungsjahr: 2012 Herausgeber: LAP LAMBERT Academic Publishing
Buch in der Datenbank seit 2008-08-13T17:53:50+02:00 (Berlin) Detailseite zuletzt geändert am 2024-01-31T14:42:07+01:00 (Berlin) ISBN/EAN: 9783659118890
ISBN - alternative Schreibweisen: 3-659-11889-3, 978-3-659-11889-0 Alternative Schreibweisen und verwandte Suchbegriffe: Titel des Buches: machine learning, applications finance
