Data mining : practical machine learning tools and techniques /

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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 acclaime...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Witten, I. H. (Ian H.) (Autor), Frank, Eibe (Autor), Hall, Mark A. (Mark Andrew) (Autor), Pal, Christopher J. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA, United States : Morgan Kaufmann, [2017]
Edición:Fourth edition
Colección:Morgan Kaufmann series in data management systems.
Temas:
Data mining.
Data Mining
Exploration de données (Informatique)
COMPUTERS > General.
Graphical & digital media applications.
Machine learning.
Library, archive & information management.
Information technology: general issues.
Databases.
Enterprise software.
Data capture & analysis.
Artificial intelligence.
Business & Management.
Computers and IT.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 What's it all about?
  • 1.1. Data Mining and Machine Learning
  • Describing Structural Patterns
  • Machine Learning
  • Data Mining
  • 1.2. Simple Examples: The Weather Problem and Others
  • Weather Problem
  • Contact Lenses: An Idealized Problem
  • Irises: A Classic Numeric Dataset
  • CPU Performance: Introducing Numeric Prediction
  • Labor Negotiations: A More Realistic Example
  • Soybean Classification: A Classic Machine Learning Success
  • 1.3. Fielded Applications
  • Web Mining
  • Decisions Involving Judgment
  • Screening Images
  • Load Forecasting
  • Diagnosis
  • Marketing and Sales
  • Other Applications
  • 1.4. Data Mining Process
  • 1.5. Machine Learning and Statistics
  • 1.6. Generalization as Search
  • Enumerating the Concept Space
  • Bias
  • 1.7. Data Mining and Ethics
  • Reidentification
  • Using Personal Information
  • Wider Issues
  • 1.8. Further Reading and Bibliographic Notes
  • ch. 2 Input: concepts, instances, attributes
  • 2.1. What's a Concept?
  • 2.2. What's in an Example?
  • Relations
  • Other Example Types
  • 2.3. What's in an Attribute?
  • 2.4. Preparing the Input
  • Gathering the Data Together
  • ARFF Format
  • Sparse Data
  • Attribute Types
  • Missing Values
  • Inaccurate Values
  • Unbalanced Data
  • Getting to Know Your Data
  • 2.5. Further Reading and Bibliographic Notes
  • ch. 3 Output: knowledge representation
  • 3.1. Tables
  • 3.2. Linear Models
  • 3.3. Trees
  • 3.4. Rules
  • Classification Rules
  • Association Rules
  • Rules With Exceptions
  • More Expressive Rules
  • 3.5. Instance-Based Representation
  • 3.6. Clusters
  • 3.7. Further Reading and Bibliographic Notes
  • ch. 4 Algorithms: the basic methods
  • 4.1. Inferring Rudimentary Rules
  • Missing Values and Numeric Attributes
  • 4.2. Simple Probabilistic Modeling
  • Missing Values and Numeric Attributes
  • Naive Bayes for Document Classification
  • Remarks
  • 4.3. Divide-and-Conquer: Constructing Decision Trees
  • Calculating Information
  • Highly Branching Attributes
  • 4.4. Covering Algorithms: Constructing Rules
  • Rules Versus Trees
  • Simple Covering Algorithm
  • Rules Versus Decision Lists
  • 4.5. Mining Association Rules
  • Item Sets
  • Association Rules
  • Generating Rules Efficiently
  • 4.6. Linear Models
  • Numeric Prediction: Linear Regression
  • Linear Classification: Logistic Regression
  • Linear Classification Using the Perceptron
  • Linear Classification Using Winnow
  • 4.7. Instance-Based Learning
  • Distance Function
  • Finding Nearest Neighbors Efficiently
  • Remarks
  • 4.8. Clustering
  • Iterative Distance-Based Clustering
  • Faster Distance Calculations
  • Choosing the Number of Clusters
  • Hierarchical Clustering
  • Example of Hierarchical Clustering
  • Incremental Clustering
  • Category Utility
  • Remarks
  • 4.9. Multi-instance Learning
  • Aggregating the Input
  • Aggregating the Output
  • 4.10. Further Reading and Bibliographic Notes
  • 4.11. WEKA Implementations
  • ch. 5 Credibility: evaluating what's been learned
  • 5.1. Training and Testing
  • 5.2. Predicting Performance
  • 5.3. Cross-Validation
  • 5.4. Other Estimates
  • Leave-One-Out
  • Bootstrap
  • 5.5. Hyperparameter Selection
  • 5.6. Comparing Data Mining Schemes
  • 5.7. Predicting Probabilities
  • Quadratic Loss Function
  • Informational Loss Function
  • Remarks
  • 5.8. Counting the Cost
  • Cost-Sensitive Classification
  • Cost-Sensitive Learning
  • Lift Charts
  • ROC Curves
  • Recall-Precision Curves
  • Remarks
  • Cost Curves
  • 5.9. Evaluating Numeric Prediction
  • 5.10. 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
  • ch. 6 Trees and rules
  • 6.1. Decision Trees
  • Numeric Attributes
  • Missing Values
  • Pruning
  • Estimating Error Rates
  • Complexity of Decision Tree Induction
  • From Trees to Rules
  • C4.5: Choices and Options
  • Cost-Complexity Pruning
  • Discussion
  • 6.2. Classification Rules
  • Criteria for Choosing Tests
  • Missing Values, Numeric Attributes
  • Generating Good Rules
  • Using Global Optimization
  • Obtaining Rules From Partial Decision Trees
  • Rules With Exceptions
  • Discussion
  • 6.3. Association Rules
  • Building a Frequent Pattern Tree
  • Finding Large Item Sets
  • Discussion
  • 6.4. WEKA Implementations
  • ch. 7 Extending instance-based and linear models
  • 7.1. Instance-Based Learning
  • Reducing the Number of Exemplars
  • Pruning Noisy Exemplars
  • Weighting Attributes
  • Generalizing Exemplars
  • Distance Functions for Generalized Exemplars
  • Generalized Distance Functions
  • Discussion
  • 7.2. Extending Linear Models
  • Maximum Margin Hyperplane
  • Nonlinear Class Boundaries
  • Support Vector Regression
  • Kernel Ridge Regression
  • Kernel Perceptron
  • Multilayer Perceptrons
  • Radial Basis Function Networks
  • Stochastic Gradient Descent
  • Discussion
  • 7.3. Numeric Prediction With Local Linear Models
  • Model Trees
  • Building the Tree
  • Pruning the Tree
  • Nominal Attributes
  • Missing Values
  • Pseudocode for Model Tree Induction
  • Rules From Model Trees
  • Locally Weighted Linear Regression
  • Discussion
  • 7.4. WEKA Implementations
  • ch. 8 Data transformations
  • 8.1. Attribute Selection
  • Scheme-Independent Selection
  • Searching the Attribute Space
  • Scheme-Specific Selection
  • 8.2. Discretizing Numeric Attributes
  • Unsupervised Discretization
  • Entropy-Based Discretization
  • Other Discretization Methods
  • Entropy-Based Versus Error-Based Discretization
  • Converting Discrete to Numeric Attributes
  • 8.3. Projections
  • Principal Component Analysis
  • Random Projections
  • Partial Least Squares Regression
  • Independent Component Analysis
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Fisher's Linear Discriminant Analysis
  • Text to Attribute Vectors
  • Time Series
  • 8.4. Sampling
  • Reservoir Sampling
  • 8.5. Cleansing
  • Improving Decision Trees
  • Robust Regression
  • Detecting Anomalies
  • One-Class Learning
  • Outlier Detection
  • Generating Artificial Data
  • 8.6. Transforming Multiple Classes to Binary Ones
  • Simple Methods
  • Error-Correcting Output Codes
  • Ensembles of Nested Dichotomies
  • 8.7. Calibrating Class Probabilities
  • 8.8. Further Reading and Bibliographic Notes
  • 8.9. WEKA Implementations
  • ch.
  • 9 Probabilistic methods
  • 9.1. Foundations
  • Maximum Likelihood Estimation
  • Maximum a Posteriori Parameter Estimation
  • 9.2. Bayesian Networks
  • Making Predictions
  • Learning Bayesian Networks
  • Specific Algorithms
  • Data Structures for Fast Learning
  • 9.3. Clustering and Probability Density Estimation
  • Expectation Maximization Algorithm for a Mixture of Gaussians
  • Extending the Mixture Model
  • Clustering Using Prior Distributions
  • Clustering With Correlated Attributes
  • Kernel Density Estimation
  • Comparing Parametric, Semiparametric and Nonparametric Density Models for Classification
  • 9.4. Hidden Variable Models
  • Expected Log-Likelihoods and Expected Gradients
  • Expectation Maximization Algorithm
  • Applying the Expectation Maximization Algorithm to Bayesian Networks
  • 9.5. Bayesian Estimation and Prediction
  • Probabilistic Inference Methods
  • 9.6. Graphical Models and Factor Graphs
  • Graphical Models and Plate Notation
  • Probabilistic Principal Component Analysis
  • Latent Semantic Analysis
  • Using Principal Component Analysis for Dimensionality Reduction
  • Probabilistic LSA
  • Latent Dirichlet Allocation
  • Factor Graphs
  • Markov Random Fields
  • Computing Using the Sum-Product and Max-Product Algorithms
  • 9.7. Conditional Probability Models
  • Linear and Polynomial Regression as Probability Models
  • Using Priors on Parameters
  • Multiclass Logistic Regression
  • Gradient Descent and Second-Order Methods
  • Generalized Linear Models
  • Making Predictions for Ordered Classes
  • Conditional Probabilistic Models Using Kernels
  • 9.8. Sequential and Temporal Models
  • Markov Models and N-gram Methods
  • Hidden Markov Models
  • Conditional Random Fields
  • 9.9. Further Reading and Bibliographic Notes
  • Software Packages and Implementations
  • 9.10. WEKA Implementations
  • ch. 10 Deep learning
  • 10.1. Deep Feedforward Networks
  • MNIST Evaluation
  • Losses and Regularization
  • Deep Layered Network Architecture
  • Activation Functions
  • Backpropagation Revisited
  • Computation Graphs and Complex Network Structures
  • Checking Backpropagation Implementations
  • 10.2. Training and Evaluating Deep Networks
  • Early Stopping
  • Validation, Cross-Validation, and Hyperparameter Tuning
  • Mini-Batch-Based Stochastic Gradient Descent
  • Pseudocode for Mini-Batch Based Stochastic Gradient Descent
  • Learning Rates and Schedules
  • Regularization With Priors on Parameters
  • Dropout
  • Batch Normalization
  • Parameter Initialization
  • Unsupervised Pretraining
  • Data Augmentation and Synthetic Transformations
  • 10.3. Convolutional Neural Networks
  • ImageNet Evaluation and Very Deep Convolutional Networks
  • From Image Filtering to Learnable Convolutional Layers
  • Convolutional Layers and Gradients
  • Pooling and Subsampling Layers and Gradients
  • Implementation
  • 10.4. Autoencoders
  • Pretraining Deep Autoencoders With RBMs
  • Denoising Autoencoders and Layerwise Training.
  • Note continued: Combining Reconstructive and Discriminative Learning
  • 10.5. Stochastic Deep Networks
  • Boltzmann Machines
  • Restricted Boltzmann Machines
  • Contrastive Divergence
  • Categorical and Continuous Variables
  • Deep Boltzmann Machines
  • Deep Belief Networks
  • 10.6. Recurrent Neural Networks
  • Exploding and Vanishing Gradients
  • Other Recurrent Network Architectures
  • 10.7. Further Reading and Bibliographic Notes
  • 10.8. Deep Learning Software and Network Implementations
  • Theano
  • Tensor Flow
  • Torch
  • Computational Network Toolkit
  • Caffe
  • Deeplearning4j
  • Other Packages: Lasagne, Keras, and cuDNN
  • 10.9. WEKA Implementations
  • ch. 11 Beyond supervised and unsupervised learning
  • 11.1. Semisupervised Learning
  • Clustering for Classification
  • Cotraining
  • EM and Cotraining
  • Neural Network Approaches
  • 11.2. Multi-instance Learning
  • Converting to Single-Instance Learning
  • Upgrading Learning Algorithms
  • Dedicated Multi-instance Methods
  • 11.3. Further Reading and Bibliographic Notes
  • 11.4. WEKA Implementations
  • ch. 12 Ensemble learning
  • 12.1. Combining Multiple Models
  • 12.2. Bagging
  • Bias-Variance Decomposition
  • Bagging With Costs
  • 12.3. Randomization
  • Randomization Versus Bagging
  • Rotation Forests
  • 12.4. Boosting
  • AdaBoost
  • Power of Boosting
  • 12.5. Additive Regression
  • Numeric Prediction
  • Additive Logistic Regression
  • 12.6. Interpretable Ensembles
  • Option Trees
  • Logistic Model Trees
  • 12.7. Stacking
  • 12.8. Further Reading and Bibliographic Notes
  • 12.9. WEKA Implementations
  • ch. 13 Moving on: applications and beyond
  • 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
  • Document Classification and Clustering
  • Information Extraction
  • Natural Language Processing
  • 13.6. Web Mining
  • Wrapper Induction
  • Page Rank
  • 13.7. Images and Speech
  • Images
  • Speech
  • 13.8. Adversarial Situations
  • 13.9. Ubiquitous Data Mining
  • 13.10. Further Reading and Bibliographic Notes
  • 13.11. WEKA Implementations.

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