《精通Java機器學習（影印版）》是由東南大學出版社出版的圖書。
基本介紹
- 書名：精通Java機器學習（影印版）
- ISBN：9787564178642
- 出版社：東南大學出版社
- 出版時間：2018-10-01
出版信息
- 出版社：東南大學出版社
- ISBN：9787564178642
- 版次：1
- 商品編碼：12438819
- 品灶備臭牌：南京乃鑽判永東南大學出版社
- 包裝：平裝
- 外文名稱：Mastering Java Machine Learning
- 開本：16開
- 出版時間：2018-10-01
- 用紙：膠版估喇朵紙
內容簡介
《付墓婆精通Java機器學習（影印版）》將為你介只判多紹關於機器學習的一批先進技術，包括分類、聚去抹鞏棵類、異常檢測、流學習、主動學習、半監督學習、機率圖模型、文本挖掘、深度學習以及大數據批和流機器學習。每章都有說明性的示例和真實案例，展示了如何利用基於墓戒Java的工具來運用這些新技術。
目錄
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
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
