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Internationale AusgabeModern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Modern Data Mining With Python : A Risk-managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
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Modern Data Mining with Python: A Risk-Managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops (Paperback or Softback)
Sprache: Englisch
Verlag: Bpb Publications 3/26/2024, 2024
ISBN 10: 9355519141 ISBN 13: 9789355519146
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
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Modern Data Mining With Python : A Risk-managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
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Modern Data Mining Algorithms in C++ and Cuda C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and Cuda C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
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Modern Data Mining With Python : A Risk-managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
Sprache: Englisch
Verlag: BPB Publications 2024-02-26, 2024
ISBN 10: 9355519141 ISBN 13: 9789355519146
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
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Modern Data Mining With Python : A Risk-managed Approach to Developing and Deploying Explainable and Efficient Algorithms Using Modelops
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Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science (Paperback or Softback)
Anbieter: BargainBookStores, Grand Rapids, MI, USA
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Paperback or Softback. Zustand: New. Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science. Book.
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
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Modern Data Mining Algorithms in C++ and Cuda C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and CUDA C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
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Modern Data Mining Algorithms in C++ and Cuda C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
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Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
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Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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Taschenbuch. Zustand: Neu. Modern Data Mining Algorithms in C++ and CUDA C | Recent Developments in Feature Extraction and Selection Algorithms for Data Science | Timothy Masters | Taschenbuch | ix | Englisch | 2020 | Apress | EAN 9781484259870 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Modern Data Mining Algorithms in C++ and CUDA C
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables.As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. Yoüll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysisLocal feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversionAll algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code.The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it.What You Will LearnCombine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set.Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods.Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input.Who This Book Is ForIntermediate to advanced data science programmers and analysts.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 240 pp. Englisch.
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Taschenbuch. Zustand: Neu. Data Mining Algorithms in C++ | Data Patterns and Algorithms for Modern Applications | Timothy Masters | Taschenbuch | xiv | Englisch | 2017 | Apress | EAN 9781484233146 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps (English Edition)
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
EUR 46,93
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In den WarenkorbPaperback. Zustand: Brand New. 438 pages. 9.25x7.50x0.99 inches. In Stock. This item is printed on demand.
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Modern Data Mining Algorithms in C++ and CUDA C
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are:Forward selection component analysis Local feature selectionLinking features and a target with a hidden Markov modelImprovements on traditional stepwise selectionNominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code.The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it.What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets.Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is ForIntermediate to advanced data science programmers and analysts. 240 pp. Englisch.
