Zustand: Very good.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 21,53
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In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978170955 ISBN 13: 9781978170957
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 21,57
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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Sprache: Englisch
Verlag: The Pragmatic Programmers, United States, Raleigh, 2019
ISBN 10: 168050620X ISBN 13: 9781680506204
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
EUR 49,12
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In den WarenkorbPaperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 49,17
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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In den WarenkorbZustand: New. In.
Zustand: New. *Price HAS BEEN REDUCED by 10% until Monday, May 11 (weekend SALE item)* 378 pp., hardcover, new, THIS IS THE 2019 PRINTING. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. 218 pages. 9.50x7.75x0.75 inches. In Stock.
Anbieter: moluna, Greven, Deutschland
EUR 28,32
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In den WarenkorbKartoniert / Broschiert. Zustand: New. KlappentextRefuel your AI Models and ML applications with High-Quality Optimization and Search SolutionsKey FeaturesComplete coverage on practical implementation of genetic algorithms.Intuitive explanations and visualizations supply theo.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 66,12
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In den WarenkorbZustand: New. In English.
Zustand: very good. Le livre peut montrer des signes d'usure dus a une utilisation constante, etre marque, porter des marques d'identification ou presenter plusieurs dommages esthetiques mineurs. vendeur professionnel; envoi soigne en 24/48h.
Sprache: Englisch
Verlag: Createspace Independent Publishing Platform Okt 2017, 2017
ISBN 10: 1978170955 ISBN 13: 9781978170957
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2017 in the subject Engineering - Computer Engineering, grade: 100.00/100.00, Çukurova University, language: English, abstract: The purpose of this thesis is twofold. The first purpose is to develop new hybrid feature selection-based maximal oxygen uptake (VO2max) prediction models using for the first time the double and triple combinations of maximal, submaximal and questionnaire variables. Several machine learning methods including Support Vector Machine, artificial neural network-based and tree-structured methods combined individually with three feature selectors Relief-F, minimum redundancy maximum relevance (mRMR) and maximum-likelihood feature selector (MLFS) have been applied for model development. The second purpose is to design a new ensemble feature selector, which aggregates the consensus properties of Relief-F, mRMR and MLFS to produce more robust decisions about the set of relevantly identified VO2max predictors and to create more accurate prediction models. Using 10-fold cross validation on three different datasets, the performance of prediction models has been evaluated by calculating their multiple correlation coefficients (R¿s) and root mean squared errors (RMSE¿s). The results show that compared with the results of the other regular feature selection-based models in literature, the reported values of R and RMSE of the hybrid models in this thesis are considerably more accurate. Furthermore, prediction models based on the proposed ensemble feature selector outperform the models created by individually using the Relief-F, mRMR or MLFS, achieving similar or ideally up to 12.46% lower error rates on the average.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205511029 ISBN 13: 9786205511022
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Predicting Crude Oil Price in Nigeria with Machine Learning Models | Machine Learning Algorithms | Tayo Ogundunmade (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205511022 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: Pragmatic Programmers Feb 2019, 2019
ISBN 10: 168050620X ISBN 13: 9781680506204
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally.
Taschenbuch. Zustand: Neu. Development of New Hybrid Models for Prediction of Maximal Oxygen Uptake (VO2max) Using Machine Learning Methods Combined with Feature Selection Algorithms | Fatih Abut | Taschenbuch | Englisch | 2022 | GRIN Verlag | EAN 9783346551078 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Genetic Algorithms and Machine Learning for Programmers | Create AI Models and Evolve Solutions | Frances Buontempo | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | Pragmatic Programmers | EAN 9781680506204 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 123,95
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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 131,63
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In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Morgan & Claypool Publishers, 2013
ISBN 10: 162705197X ISBN 13: 9781627051972
Anbieter: Studibuch, Stuttgart, Deutschland
paperback. Zustand: Gut. 192 Seiten; 9781627051972.3 Gewicht in Gramm: 500.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 150,83
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In den WarenkorbZustand: New. In.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 150,83
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In den WarenkorbZustand: New. In.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 209,57
Anzahl: 1 verfügbar
In den WarenkorbZustand: New. pp. 358.
Taschenbuch. Zustand: Neu. Machine Learning Models and Algorithms for Big Data Classification | Thinking with Examples for Effective Learning | Shan Suthaharan | Taschenbuch | xix | Englisch | 2016 | Springer US | EAN 9781489978523 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer US, Springer New York, 2015
ISBN 10: 148997640X ISBN 13: 9781489976406
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 243,92
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 359 pages. 9.25x6.10x0.90 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 245,89
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In den WarenkorbHardcover. Zustand: Brand New. 9.75x6.50x1.25 inches. In Stock.