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Applied Machine Learning [Hardback]

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  • Formāts: Hardback, 656 pages, height x width x depth: 262x203x39 mm, weight: 1492 g, Illustrations
  • Izdošanas datums: 19-May-2019
  • Izdevniecība: McGraw-Hill Education
  • ISBN-10: 1260456846
  • ISBN-13: 9781260456844
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Applied Machine LearningPalielināt
  • Formāts: Hardback, 656 pages, height x width x depth: 262x203x39 mm, weight: 1492 g, Illustrations
  • Izdošanas datums: 19-May-2019
  • Izdevniecība: McGraw-Hill Education
  • ISBN-10: 1260456846
  • ISBN-13: 9781260456844
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781260456844.html
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Cutting-edge machine learning principles, practices, and applications

This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed.

Coverage includes:

•Supervised learning
•Statistical learning
•Learning with support vector machines (SVM)
•Learning with neural networks (NN)
•Fuzzy inference systems
•Data clustering
•Data transformations
•Decision tree learning
•Business intelligence
•Data mining
•And much more

Preface xvii
Acknowledgements xxi
1 Introduction 1(35)
1.1 Towards Intelligent Machines
1(4)
1.2 Well-Posed Machine Learning Problems
5(2)
1.3 Examples of Applications in Diverse Fields
7(5)
1.4 Data Representation
12(6)
1.4.1 Time Series Forecasting
15(2)
1.4.2 Datasets for Toy (Unreastically Simple) and Realistic Problems
17(1)
1.5 Domain Knowledge for Productive use of Machine Learning
18(2)
1.6 Diversity of Data: Structured/Unstructured
20(1)
1.7 Forms of Learning
21(4)
1.7.1 Supervised/Directed Learning
21(1)
1.7.2 Unsupervised/Undirected Learning
22(1)
1.7.3 Reinforcement Learning
22(1)
1.7.4 Learning Based on Natural Processes: Evolution, Swarming, and Immune Systems
23(2)
1.8 Machine Learning and Data Mining
25(1)
1.9 Basic Linear Algebra in Machine Learning Techniques
26(8)
1.10 Relevant Resources for Machine Learning
34(2)
2 Supervised Learning: Rationale and Basics 36(37)
2.1 Learning from Observations
36(6)
2.2 Bias and Variance
42(4)
2.3 Why Learning Works: Computational Learning Theory
46(3)
2.4 Occam's Razor Principle and Overfitting Avoidance
49(2)
2.5 Heuristic Search in Inductive Learning
51(5)
2.5.1 Search through Hypothesis Space
52(1)
2.5.2 Ensemble Learning
53(2)
2.5.3 Evaluation of a Learning System
55(1)
2.6 Estimating Generalization Errors
56(3)
2.6.1 Holdout Method and Random Subsampling
56(1)
2.6.2 Cross-validation
57(1)
2.6.3 Bootstrapping
58(1)
2.7 Metrics for Assessing Regression (Numeric Prediction) Accuracy
59(2)
2.7.1 Mean Square Error
60(1)
2.7.2 Mean Absolute Error
60(1)
2.8 Metrics for Assessing Classification (Pattern Recognition) Accuracy
61(7)
2.8.1 Misclassification Error
61(1)
2.8.2 Confusion Matrix
62(4)
2.8.3 Comparing Classifiers Based on ROC Curves
66(2)
2.9 An Overview of the Design Cycle and Issues in Machine Learning
68(5)
3 Statistical Learning 73(57)
3.1 Machine Learning and Inferential Statistical Analysis
73(1)
3.2 Descriptive Statistics in Learning Techniques
74(13)
3.2.1 Representing Uncertainties in Data: Probability Distributions
75(5)
3.2.2 Descriptive Measures of Probability Distributions
80(3)
3.2.3 Descriptive Measures from Data Sample
83(1)
3.2.4 Normal Distributions
84(1)
3.2.5 Data Similarity
85(2)
3.3 Bayesian Reasoning: A Probabilistic Approach to Inference
87(15)
3.3.1 Bayes Theorem
88(5)
3.3.2 Naive Bayes Classifier
93(5)
3.3.3 Bayesian Belief Networks
98(4)
3.4 k-Nearest Neighbor (k-NN) Classifier
102(4)
3.5 Discriminant Functions and Regression Functions
106(6)
3.5.1 Classification and Discriminant Functions
107(1)
3.5.2 Numeric Prediction and Regression Functions
108(1)
3.5.3 Practical Hypothesis Functions
109(3)
3.6 Linear Regression with Least Square Error Criterion
112(4)
3.6.1 Minimal Sum-of-Error-Squares and the Pseudoinverse
113(2)
3.6.2 Gradient Descent Optimization Schemes
115(1)
3.6.3 Least Mean Square (LMS) Algorithm
115(1)
3.7 Logistic Regression for Classification Tasks
116(4)
3.8 Fisher's Linear Discriminant and Thresholding for Classification
120(6)
3.8.1 Fisher's Linear Discriminant
120(5)
3.8.2 Thresholding
125(1)
3.9 Minimum Description Length Principle
126(4)
3.9.1 Bayesian Perspective
127(1)
3.9.2 Entropy and Information
128(2)
4 Learning With Support Vector Machines (SVM) 130(51)
4.1 Introduction
130(2)
4.2 Linear Discriminant Functions for Binary Classification
132(4)
4.3 Perceptron Algorithm
136(5)
4.4 Linear Maximal Margin Classifier for Linearly Separable Data
141(11)
4.5 Linear Soft Margin Classifier for Overlapping Classes
152(6)
4.6 Kernel-Induced Feature Spaces
158(4)
4.7 Nonlinear Classifier
162(5)
4.8 Regression by Support Vector Machines
167(7)
4.8.1 Linear Regression
169(3)
4.8.2 Nonlinear Regression
172(2)
4.9 Decomposing Multiclass Classification Problem Into Binary Classification Tasks
174(3)
4.9.1 One-Against-All (OAA)
175(1)
4.9.2 One-Against-One (0A0)
176(1)
4.10 Variants of Basic SVM Techniques
177(4)
5 Learning With Neural Networks (NN) 181(64)
5.1 Towards Cognitive Machine
181(3)
5.1.1 From Perceptrons to Deep Networks
182(2)
5.2 Neuron Models
184(9)
5.2.1 Biological Neuron
184(2)
5.2.2 Artificial Neuron
186(4)
5.2.3 Mathmatical Model
190(3)
5.3 Network Architectures
193(7)
5.3.1 Feedforward Networks
194(5)
5.3.2 Recurrent Networks
199(1)
5.4 Perceptrons
200(6)
5.4.1 Limitations of Perceptron Algorithm for Linear Classification Tasks
201(1)
5.4.2 Linear Classification using Regression Techniques
201(2)
5.4.3 Standard Gradient Descent Optimization Scheme: Steepest Descent
203(3)
5.5 Linear Neuron and the Widrow-Hoff Learning Rule
206(2)
5.5.1 Stochastic Gradient Descent
207(1)
5.6 The Error-Correction Delta Rule
208(5)
5.6.1 Sigmoid Unit: Soft-Limiting Perceptron
211(2)
5.7 Multi-Layer Perceptron (MLP) Networks and the Error-Backpropagation Algorithm
213(19)
5.7.1 The Generalized Delta Rule
216(10)
5.7.2 Convergence and Local Minima
226(1)
5.7.3 Adding Momentum to Gradient Descent
227(1)
5.7.4 Heuristic Aspects of the Error-backpropagation Algorithm
228(4)
5.8 Multi-Class Discrimination with MLP Networks
232(3)
5.9 Radial Basis Functions (RBF) Networks
235(6)
5.9.1 Training the RBF Network
239(2)
5.10 Genetic-Neural Systems
241(4)
6 Fuzzy Inference Systems 245(83)
6.1 Introduction
245(3)
6.2 Cognitive Uncertainty and Fuzzy Rule-Base
248(5)
6.3 Fuzzy Quantification of Knowledge
253(24)
6.3.1 Fuzzy Logic
253(4)
6.3.2 Fuzzy Sets
257(10)
6.3.3 Fuzzy Set Operations
267(1)
6.3.4 Fuzzy Relations
268(9)
6.4 Fuzzy Rule-Base and Approximate Reasoning
277(24)
6.4.1 Quantification of Rules via Fuzzy Relations
281(2)
6.4.2 Fuzzification of Input
283(1)
6.4.3 Inference Mechanism
284(14)
6.4.4 Defuzzification of Inferred Fuzzy Set
298(3)
6.5 Mamdani Model for Fuzzy Inference Systems
301(10)
6.5.1 Mobile Robot Navigation Among Moving Obstacles
303(5)
6.5.2 Mortgage Loan Assessment
308(3)
6.6 Takagi-Sugeno Fuzzy Model
311(6)
6.7 Neuro-Fuzzy Inference Systems
317(7)
6.7.1 ANFIS Architecture
318(2)
6.7.2 How Does an ANFIS Learn?
320(4)
6.8 Gentic-Fuzzy Systems
324(4)
7 Data Clustering and Data Transformations 328(76)
7.1 Unsupervised Learning
328(3)
7.1.1 Clustering
329(2)
7.2 Engineering the Data
331(10)
7.2.1 Exploratory Data Analysis: Learning about What is in the Data
333(1)
7.2.2 Cluster Analysis: Finding Similarities in the Data
334(5)
7.2.3 Data Transformations: Enhancing the Information Content of the Data
339(2)
7.3 Overview of Basic Clustering Methods
341(11)
7.3.1 Partitional Clustering
341(3)
7.3.2 Hierarchical Clustering
344(1)
7.3.3 Spectral Clustering
345(4)
7.3.4 Clustering using Self-Organizing Maps
349(3)
7.4 K-Means Clustering
352(4)
7.5 Fuzzy K-Means Clustering
356(6)
7.6 Expectation-Maximization (EM) Algorithm and Gaussian Mixtures Clustering
362(10)
7.6.1 EM Algorithm
362(3)
7.6.2 Gaussian Mixture Models
365(7)
7.7 Some Useful Data Transformations
372(5)
7.7.1 Data Cleansing
372(2)
7.7.2 Derived Attributes
374(1)
7.7.3 Discretizing Numeric Attributes
375(2)
7.7.4 Attribute Reduction Techniques
377(1)
7.8 Entropy-Based Method for Attribute Discretization
377(5)
7.9 Principal Components Analysis (PCA) for Attribute Reduction
382(8)
7.10 Rough Sets-Based Methods for Attribute Reduction
390(14)
7.10.1 Rough Set Preliminaries
392(5)
7.10.2 Analysis of Relevance of Attributes
397(2)
7.10.3 Reduction of Attributes
399(5)
8 Decision Tree Learning 404(41)
8.1 Introduction
404(2)
8.2 Example of a Classification Decision Tree
406(5)
8.3 Measures of Impurity for Evaluating Splits in Decision Trees
411(7)
8.3.1 Information Gain/Entropy reduction
411(5)
8.3.2 Gain Ratio
416(1)
8.3.3 Gini Index
417(1)
8.4 ID3, C4.5, and CART Decision Trees
418(9)
8.5 Pruning the Tree
427(2)
8.6 Strengths and Weaknesses of Decision-Tree Approach
429(4)
8.7 Fuzzy Decision Trees
433(12)
9 Business Intelligence and Data Mining: Techniques and Applications 445(63)
9.1 An Introduction to Analytics
445(6)
9.1.1 Machine Learning, Data Mining, and Predictive Analytics
448(1)
9.1.2 Basic Analytics Techniques
449(2)
9.2 The CRISP-DM (Cross Industry Standard Process for Data Mining) Model
451(5)
9.3 Data Warehousing and Online Analytical Processing
456(11)
9.3.1 Basic Concepts
456(2)
9.3.2 Databases
458(3)
9.3.3 Data Warehousing: A General Architecture, and OLAP Operations
461(5)
9.3.4 Data Mining in the Data Warehouse Environment
466(1)
9.4 Mining Frequent Patterns and Association Rules
467(12)
9.4.1 Basic Concepts
469(2)
9.4.2 Measures of Strength of Frequent Patterns and Association Rules
471(2)
9.4.3 Frequent Item Set Mining Methods
473(4)
9.4.4 Generating Association Rules from Frequent Itemsets
477(2)
9.5 Intelligent Information Retrieval Systems
479(11)
9.5.1 Text Retrieval
483(3)
9.5.2 Image Retrieval
486(2)
9.5.3 Audio Retrieval
488(2)
9.6 Applications and Trends
490(8)
9.6.1 Data Mining Applications
490(5)
9.6.2 Data Mining Trends
495(3)
9.7 Technologies for Big Data
498(10)
9.7.1 Emerging Analytic Methods
500(3)
9.7.2 Emerging Technologies for Higher Levels of Scalability
503(5)
Appendix A Genetic Algorithm (GA) For Search Optimization 508(19)
A.1 A Simple Overview of Genetics
510(1)
A.2 Genetics on Computers
511(4)
A.3 The Basic Genetic Algorithm
515(9)
A.4 Beyond the Basic Genetic Algorithm
524(3)
Appendix B Reinforcement Learning (RL) 527(22)
B.1 Introduction
527(3)
B.2 Elements of Reinforcement Learning
530(5)
B.3 Basics of Dynamic Programming
535(7)
B.3.1 Finding Optimal Policies
538(1)
B.3.2 Value Iteration
539(1)
B.3.3 Policy Iteration
540(2)
B.4 Temporal Difference Learning
542(7)
B.4.1 Q-learning
544(2)
B.4.2 Generalization
546(2)
B.4.3 Sarsa-learning
548(1)
Datasets from Real-Life Applications for Machine Learning Experiments 549(18)
Problems 567(46)
References 613(10)
Index 623