作者：UdayKamath
页数：21,519页
出版社：东南大学出版社
出版日期：2018
ISBN：9787564178642
电子书格式：pdf/epub/txt
内容简介
《精通Java机器学习（影印版）》将为你介绍关于机器学习的一批先进技术，包括分类、聚类、异常检测、流学习、主动学习、半监督学习、概率图模型、文本挖掘、深度学习以及大数据批和流机器学习。每章都有说明性的示例和真实案例，展示了如何利用基于Java的工具来运用这些新技术。
本书特色
《精通Java机器学习（影印版）》将为你介绍关于机器学习的一批先进技术，包括分类、聚类、异常检测、流学习、主动学习、半监督学习、概率图模型、文本挖掘、深度学习以及大数据批和流机器学习。每章都有说明性的示例和真实案例，展示了如何利用基于Java的工具来运用这些新技术。
目录
Preface
Chapter 1: Machine Learning Review
Machine learning – history and definition
What is not machine learning
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
Roles
Process
Machine learning -tools and datasets
Datasets
Summary
Chapter 2: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data quality analysis
Descriptive data analysis
Basic label analysis
Basic feature analysis
Visualization analysis
Univariate feature analysis
Multivariate feature analysis
Data transformation and preprocessing
Feature construction
Handling missing values
Outliers
Discretization
Data sampling
Is sampling needed
Undersampling and oversampling
Training, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniques
Feature evaluation techniques
Filter approach
Wrapper approach
Embedded approach
Model building
Linear models
Linear Regression
Naive Bayes
Logistic Regression
Non-linear models
Decision Trees
K-Nearest Neighbors (KNN)
Support vector machines (SVM)
Ensemble learning and meta learners
Bootstrap aggregating or bagging
Boosting
Model assessment, evaluation, and comparisons
Model assessment
Model evaluation metrics
Confusion matrix and related metrics
ROC and PRC curves
Gain charts and lift curves
Model comparisons
Comparing two algorithms
Comparing multiple algorithms
Case Study – Horse Colic Classification
Business problem
Machine learning mapping
Data analysis
Label analysis
Features analysis
Supervised learning experiments
Weka experiments
RapidMiner experiments
Results, observations, and analysis
Summary
References
Chapter 3: Unsupervised Machine Learninq Techniques
……
Chapter 4: Semi-Supervised and Active Learning
Chapter 5: Real-Time Stream Machine Learning
Chapter 6: Probabilistic Graph Modeling
Chapter 7: Deep Learning
Chapter 8: Text Mining and Natural Language Processing
Chapter 9: Bia Data Machine Learnina – The Final Frontier
Appendix A: Linear Algebra
Appendix B: Probability
Index
Chapter 1: Machine Learning Review
Machine learning – history and definition
What is not machine learning
Machine learning – concepts and terminology
Machine learning – types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning – roles and process
Roles
Process
Machine learning -tools and datasets
Datasets
Summary
Chapter 2: Practical Approach to Real-World Supervised Learning
Formal description and notation
Data quality analysis
Descriptive data analysis
Basic label analysis
Basic feature analysis
Visualization analysis
Univariate feature analysis
Multivariate feature analysis
Data transformation and preprocessing
Feature construction
Handling missing values
Outliers
Discretization
Data sampling
Is sampling needed
Undersampling and oversampling
Training, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniques
Feature evaluation techniques
Filter approach
Wrapper approach
Embedded approach
Model building
Linear models
Linear Regression
Naive Bayes
Logistic Regression
Non-linear models
Decision Trees
K-Nearest Neighbors (KNN)
Support vector machines (SVM)
Ensemble learning and meta learners
Bootstrap aggregating or bagging
Boosting
Model assessment, evaluation, and comparisons
Model assessment
Model evaluation metrics
Confusion matrix and related metrics
ROC and PRC curves
Gain charts and lift curves
Model comparisons
Comparing two algorithms
Comparing multiple algorithms
Case Study – Horse Colic Classification
Business problem
Machine learning mapping
Data analysis
Label analysis
Features analysis
Supervised learning experiments
Weka experiments
RapidMiner experiments
Results, observations, and analysis
Summary
References
Chapter 3: Unsupervised Machine Learninq Techniques
……
Chapter 4: Semi-Supervised and Active Learning
Chapter 5: Real-Time Stream Machine Learning
Chapter 6: Probabilistic Graph Modeling
Chapter 7: Deep Learning
Chapter 8: Text Mining and Natural Language Processing
Chapter 9: Bia Data Machine Learnina – The Final Frontier
Appendix A: Linear Algebra
Appendix B: Probability
Index
