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Data Mining

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Efnisyfirlit

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • List of Figures
  • List of Tables
  • Preface
    • Updated and Revised Content
    • Acknowledgments
  • Part I: Introduction to data mining
    • Chapter 1. What’s it all about?
      • Abstract
      • 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
    • Chapter 2. Input: Concepts, instances, attributes
      • Abstract
      • 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
    • Chapter 3. Output: Knowledge representation
      • Abstract
      • 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
    • Chapter 4. Algorithms: The basic methods
      • Abstracts
      • 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
    • Chapter 5. Credibility: Evaluating what’s been learned
      • Abstract
      • 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 MDL Principle
      • 5.11 Applying the MDL Principle to Clustering
      • 5.12 Using a Validation Set for Model Selection
      • 5.13 Further Reading and Bibliographic Notes
  • Part II: More advanced machine learning schemes
    • Part II. More advanced machine learning schemes
    • Chapter 6. Trees and rules
      • Abstract
      • 6.1 Decision Trees
      • 6.2 Classification Rules
      • 6.3 Association Rules
      • 6.4 Weka Implementations
    • Chapter 7. Extending instance-based and linear models
      • Abstract
      • 7.1 Instance-Based Learning
      • 7.2 Extending Linear Models
      • 7.3 Numeric Prediction With Local Linear Models
      • 7.4 Weka Implementations
    • Chapter 8. Data transformations
      • Abstracts
      • 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 Bibliographic Notes
      • 8.9 Weka Implementations
    • Chapter 9. Probabilistic methods
      • Abstract
      • 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
    • Chapter 10. Deep learning
      • Abstract
      • 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
    • Chapter 11. Beyond supervised and unsupervised learning
      • Abstract
      • 11.1 Semisupervised Learning
      • 11.2 Multi-instance Learning
      • 11.3 Further Reading and Bibliographic Notes
      • 11.4 WEKA Implementations
    • Chapter 12. Ensemble learning
      • Abstract
      • 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
    • Chapter 13. Moving on: applications and beyond
      • Abstract
      • 13.1 Applying Machine Learning
      • 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
  • Appendix A. Theoretical foundations
    • A.1 Matrix Algebra
    • A.2 Fundamental Elements of Probabilistic Methods
  • Appendix B. The WEKA workbench
    • B.1 What’s in WEKA?
    • B.2 The package management system
    • B.3 The Explorer
    • B.4 The Knowledge Flow Interface
    • B.5 The Experimenter
  • References
  • Index

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Vörumerki: Elsevier
Vörunúmer: 9780128043578
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Data Mining

Vörumerki: Elsevier
Vörunúmer: 9780128043578
Rafræn bók. Uppl. sendar á netfangið þitt eftir kaup

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4.590 kr.
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4.590 kr.