Dirk Husmeier (40 results)
- Softcover
Seller: -OnTimeBooks-, Phoenix, AZ, U.S.A.-OnTimeBooks-
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Condition: good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipp…ed with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.
- Softcover
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Condition: very_good. This books is in Very good condition. There may be a few flaws like shelf wear and some light wear.
- Softcover
Seller: Phatpocket Limited, Waltham Abbey, HERTS, United KingdomPhatpocket Limited
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Condition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Softcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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Paperback. Condition: New.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. pp. 302.
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 275 pages. 9.50x6.25x0.75 inches. In Stock.
Language: English
Published by London ; Berlin ; Tokyo ; Heidelberg ; New York ; Barcelona ; Hong Kong ; Milan ; Paris ; Santa Clara ; Singapore : Springer, 1999
- Softcover
Seller: Roland Antiquariat UG haftungsbeschränkt, Weinheim, GermanyRoland Antiquariat UG haftungsbeschränkt
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US$ 65.76
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Softcover. XXIII, 275 S. : graph. Darst. ; 24 cm Like new. Unread book. --- Neuwertiger Zustand. Ungelesenes Buch. 9781852330958 Sprache: Deutsch Gewicht in Gramm: 467 Softcover reprint of the original 1st ed. 1999.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 70.43
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of…past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
Probabilistic Modeling in Bioinformatics and Medical Informatics
Husmeier, Dirk (EDT); Dybowski, Richard (EDT); Roberts, Stephen (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
Probabilistic Modeling in Bioinformatics and Medical Informatics
Husmeier, Dirk (EDT); Dybowski, Richard (EDT); Roberts, Stephen (EDT)
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: As New. Unread book in perfect condition.
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condition: New.
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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US$ 164.52
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Taschenbuch. Condition: Neu. Probabilistic Modeling in Bioinformatics and Medical Informatics | Dirk Husmeier (u. a.) | Taschenbuch | Advanced Information and Knowledge Processing | xx | Englisch | 2010 | Springer | EAN 9781849969123 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg,…juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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US$ 235.24
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Condition: New. In.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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US$ 244.35
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Condition: New. pp. 526.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. pp. 528.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 198.14
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the…methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
US$ 198.14
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodo…logy of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a… time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5. 300 pp. Englisch.
- Softcover
- Print on Demand
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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US$ 90.10
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Condition: New. Print on Demand pp. 302 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: New. PRINT ON DEMAND pp. 302.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides unique, comprehensive coverage of generalisation and regularisation: Provides the first real-world test results for recent theoretical findings on the generalisation performance of committeesConventional appl…ications of neural networks usually .
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a tim…e series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 300 pp. Englisch.
