Data mining / Ian H. Witten  

Public ISBD Titre : Data mining : Practical machine learning tools and techniques Type de document : texte imprimé Auteurs : Ian H. Witten, Auteur ; Eibe Frank, Auteur ; Mark A. Hall, Auteur ; Christopher J. Pal, Auteur Mention d'édition : Fourth Edition Editeur : Amsterdam : Elsevier Année de publication : 2017 Importance : 1 vol. (621 p.) Format : 24 cm ISBN/ISSN/EAN : 978-0-12-804291-5 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Exploration de données Bases de données :Interrogation Bases de données Exploration de données Bases de données -- Interrogation Bases de données Data mining Résumé : Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc Note de contenu : Table of Contents of the 4th Edition: Preface 1. What’s it all about? 1.1 Data Mining and Machine Learning 1.2 Simple Examples: The Weather Problem and Others 1.3 Fielded Applications 1.4 The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search 1.7 Data Mining and Ethics 1.8 Further Reading and Bibliographic Notes 2. Input: concepts, instances, attributes 2.1 What’s a Concept? 2.2 What’s in an Example? 2.3 What’s in an Attribute? 2.4 Preparing the Input 2.5 Further Reading and Bibliographic Notes 3. Output: Knowledge representation 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading and Bibliographic Notes 4. Algorithms: the basic methods 4.1 Inferring Rudimentary Rules 4.2 Simple Probabilistic Modeling 4.3 Divide-and-Conquer: Constructing Decision Trees 4.4 Covering Algorithms: Constructing Rules 4.5 Mining Association Rules 4.6 Linear Models 4.7 Instance-Based Learning 4.8 Clustering 4.9 Multi-Instance Learning 4.10 Further Reading and Bibliographic Notes 4.11 WEKA Implementations 5. Credibility: Evaluating what’s been learned 5.1 Training and Testing 5.2 Predicting Performance 5.3 Cross-Validation 5.4 Other Estimates 5.5 Hyperparameter Selection 5.6 Comparing Data Mining Schemes 5.7 Predicting Probabilities 5.8 Counting the Cost 5.9 Evaluating Numeric Prediction 5.10 The Minimum Description Length Principle 5.11 Applying MDL to Clustering 5.12 Using a Validation Set for Model Selection 5.13 Further Reading and Bibliographic Notes 6. Trees and rules 6.1 Decision Trees 6.2 Classification Rules 6.3 Association Rules 6.4 WEKA Implementations 7. Extending instance-based and linear models 7.1 Instance-Based Learning 7.2 Extending Linear Models 7.3 Numeric Prediction with Local Linear Models 7.4 WEKA Implementations 8. Data transformations 8.1 Attribute Selection 8.2 Discretizing Numeric Attributes 8.3 Projections 8.4 Sampling 8.5 Cleansing 8.6 Transforming Multiple Classes to Binary Ones 8.7 Calibrating Class Probabilities 8.8 Further Reading and Biblographic Notes 8.9 WEKA Implementations 9. Probabilistic methods 9.1 Foundations 9.2 Bayesian Networks 9.3 Clustering and Probability Density Estimation 9.4 Hidden Variable Models 9.5 Bayesian Estimation and Prediction 9.6 Graphical Models and Factor Graphs 9.7 Conditional Probability Models 9.8 Sequential and Temporal Models 9.9 Further Reading and Bibliographic Notes 9.10 WEKA Implementations 10. Deep learning 10.1 Deep Feedforward Networks 10.2 Training and Evaluating Deep Networks 10.3 Convolutional Neural Networks 10.4 Autoencoders 10.5 Stochastic Deep Networks 10.6 Recurrent Neural Networks 10.7 Further Reading and Bibliographic Notes 10.8 Deep Learning Software and Network Implementations 10.9 WEKA implementations 11. Beyond supervised and unsupervised learning 11.1 Semi-supervised learning 11.2 Multi-instance Learning 11.3 Further Reading and Bibliographic Notes 11.4 WEKA Implementations 12. Ensemble Learning 12.1 Combining Multiple Models 12.2 Bagging 12.3 Randomization 12.4 Boosting 12.5 Additive Regression 12.6 Interpretable Ensembles 12.7 Stacking 12.8 Further Reading and Bibliographic Notes 12.9 WEKA Implementations 13. Moving on: Applications and Beyond 13.1 Applying Data Mining 13.2 Learning from Massive Datasets 13.3 Data Stream Learning 13.4 Incorporating Domain Knowledge 13.5 Text Mining 13.6 Web Mining 13.7 Images and Speech 13.8 Adversarial Situations 13.9 Ubiquitous Data Mining 13.10 Further Reading and Bibliographic Notes 13.11 WEKA Implementations Index Côte titre : Fs/19728 En ligne : https://www.amazon.com/exec/obidos/ASIN/0128042915/departmofcompute?asin=0128042 [...] Data mining : Practical machine learning tools and techniques [texte imprimé] / Ian H. Witten, Auteur ; Eibe Frank, Auteur ; Mark A. Hall, Auteur ; Christopher J. Pal, Auteur  . -  Fourth Edition . - Amsterdam : Elsevier, 2017 . - 1 vol. (621 p.) ; 24 cm. ISBN : 978-0-12-804291-5 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Exploration de données Bases de données :Interrogation Bases de données Exploration de données Bases de données -- Interrogation Bases de données Data mining Résumé : Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the bookOnline Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the bookTable of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc Note de contenu : Table of Contents of the 4th Edition: Preface 1. What’s it all about? 1.1 Data Mining and Machine Learning 1.2 Simple Examples: The Weather Problem and Others 1.3 Fielded Applications 1.4 The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search 1.7 Data Mining and Ethics 1.8 Further Reading and Bibliographic Notes 2. Input: concepts, instances, attributes 2.1 What’s a Concept? 2.2 What’s in an Example? 2.3 What’s in an Attribute? 2.4 Preparing the Input 2.5 Further Reading and Bibliographic Notes 3. Output: Knowledge representation 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading and Bibliographic Notes 4. Algorithms: the basic methods 4.1 Inferring Rudimentary Rules 4.2 Simple Probabilistic Modeling 4.3 Divide-and-Conquer: Constructing Decision Trees 4.4 Covering Algorithms: Constructing Rules 4.5 Mining Association Rules 4.6 Linear Models 4.7 Instance-Based Learning 4.8 Clustering 4.9 Multi-Instance Learning 4.10 Further Reading and Bibliographic Notes 4.11 WEKA Implementations 5. Credibility: Evaluating what’s been learned 5.1 Training and Testing 5.2 Predicting Performance 5.3 Cross-Validation 5.4 Other Estimates 5.5 Hyperparameter Selection 5.6 Comparing Data Mining Schemes 5.7 Predicting Probabilities 5.8 Counting the Cost 5.9 Evaluating Numeric Prediction 5.10 The Minimum Description Length Principle 5.11 Applying MDL to Clustering 5.12 Using a Validation Set for Model Selection 5.13 Further Reading and Bibliographic Notes 6. Trees and rules 6.1 Decision Trees 6.2 Classification Rules 6.3 Association Rules 6.4 WEKA Implementations 7. Extending instance-based and linear models 7.1 Instance-Based Learning 7.2 Extending Linear Models 7.3 Numeric Prediction with Local Linear Models 7.4 WEKA Implementations 8. Data transformations 8.1 Attribute Selection 8.2 Discretizing Numeric Attributes 8.3 Projections 8.4 Sampling 8.5 Cleansing 8.6 Transforming Multiple Classes to Binary Ones 8.7 Calibrating Class Probabilities 8.8 Further Reading and Biblographic Notes 8.9 WEKA Implementations 9. Probabilistic methods 9.1 Foundations 9.2 Bayesian Networks 9.3 Clustering and Probability Density Estimation 9.4 Hidden Variable Models 9.5 Bayesian Estimation and Prediction 9.6 Graphical Models and Factor Graphs 9.7 Conditional Probability Models 9.8 Sequential and Temporal Models 9.9 Further Reading and Bibliographic Notes 9.10 WEKA Implementations 10. Deep learning 10.1 Deep Feedforward Networks 10.2 Training and Evaluating Deep Networks 10.3 Convolutional Neural Networks 10.4 Autoencoders 10.5 Stochastic Deep Networks 10.6 Recurrent Neural Networks 10.7 Further Reading and Bibliographic Notes 10.8 Deep Learning Software and Network Implementations 10.9 WEKA implementations 11. Beyond supervised and unsupervised learning 11.1 Semi-supervised learning 11.2 Multi-instance Learning 11.3 Further Reading and Bibliographic Notes 11.4 WEKA Implementations 12. Ensemble Learning 12.1 Combining Multiple Models 12.2 Bagging 12.3 Randomization 12.4 Boosting 12.5 Additive Regression 12.6 Interpretable Ensembles 12.7 Stacking 12.8 Further Reading and Bibliographic Notes 12.9 WEKA Implementations 13. Moving on: Applications and Beyond 13.1 Applying Data Mining 13.2 Learning from Massive Datasets 13.3 Data Stream Learning 13.4 Incorporating Domain Knowledge 13.5 Text Mining 13.6 Web Mining 13.7 Images and Speech 13.8 Adversarial Situations 13.9 Ubiquitous Data Mining 13.10 Further Reading and Bibliographic Notes 13.11 WEKA Implementations Index Côte titre : Fs/19728 En ligne : https://www.amazon.com/exec/obidos/ASIN/0128042915/departmofcompute?asin=0128042 [...]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
Fs/19728	Fs/19728	Livre	Bibliothéque des sciences	Français	Disponible Sorti jusqu'au 08/05/2024

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