Evgenii Vityaev (14 results)
- Hardcover
Seller: Solibri, Epone, FranceSolibri
Contact seller4-star sellerCondition: Used - Fine
£ 4.28
£ 48.76 shippingShips from France to U.S.A.Quantity: 1 available
Condition: fine. couverture cartonnée, moyen format , très bon état. Inscriptions en page de garde. 2642907 - Data Mining in Finance: Advances in Relational and Hybrid Methods, Kovalerchuk, Boris, Springer, 2000.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
Contact seller5-star sellerCondition: New
£ 189.04
£ 11.98 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
Condition: New. In.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 244.88
£ 3.02 shippingShips within U.S.A.Quantity: 4 available
Condition: New. pp. 328.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 245.32
£ 3.02 shippingShips within U.S.A.Quantity: 4 available
Condition: New. pp. 328.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 198.32
£ 54.62 shippingShips from Germany to U.S.A.Quantity: 1 available
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these app…roaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
- Hardcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: Used - As new
£ 280.00
£ 25.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 160.06
£ 42.28 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then… examines the suitability of.
- Hardcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 160.06
£ 42.28 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then… examines the suitability of.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 190.22
£ 19.85 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the s…uitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics. 328 pp. Englisch.
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 190.22
£ 19.85 shippingShips from Germany to U.S.A.Quantity: 2 available
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitabil…ity of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics. 328 pp. Englisch.
- Hardcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 260.80
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand pp. 328 Illus.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 262.28
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand pp. 328 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 266.35
£ 8.59 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND pp. 328.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 268.09
£ 8.59 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND pp. 328.
