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Data Mining Practical Machine Learning Tools and Techniques, 4/Ed > 데이터마이닝

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Data Mining Practical Machine Learning Tools and Techniques, 4/Ed
판매가격 79,000원
저자 Ian Witten
도서종류 외국도서
출판사 Morgan Kaufmann
발행언어 영어
발행일 2016-11-16
페이지수 654
ISBN 9780128042915
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  • 도서 정보

    도서 상세설명

    Table of Contents
    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

    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
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