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-… mais…
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<
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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-… mais…
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… mais…
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.
Dados detalhados do livro - Machine Learning In Computational Finance: Practical algorithms for building artificial intelligence applications
EAN (ISBN-13): 9783659118890 ISBN (ISBN-10): 3659118893 Livro de capa dura Livro de bolso Ano de publicação: 2012 Editor/Editora: LAP LAMBERT Academic Publishing
Livro na base de dados desde 2008-08-13T12:53:50-03:00 (Sao Paulo) Página de detalhes modificada pela última vez em 2024-01-31T10:42:07-03:00 (Sao Paulo) Número ISBN/EAN: 3659118893
Número ISBN - Ortografia alternativa: 3-659-11889-3, 978-3-659-11889-0 Ortografia alternativa e termos de pesquisa relacionados: Título do livro: machine learning, applications finance
