机器学习及交通应用(MachineLearningforTransportation)
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作者编者:陈淑燕//马永锋//乔凤祥|责编:马伟
出版社东南大学
ISBN9787576603620
出版时间2022-12
装帧平装
开本其他
定价68元
货号1202822003
上书时间2024-06-02
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目录
Chapter 1 Introduction to Machine Learning
1.1 Definition of Machine Learning
1.2 History of Machine Learning
1.2.1 Artificial Intelligence, Machine Learning, and Deep Learning
1.2.2 Fields Related to Machine Learning
1.3 Workflow of Machine Learning
1.4 Types of Machine Learning Algorithms
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Semi-supervised Learning
1.4.4 Reinforced Learning
1.5 Organization of the Textbook
1.6 Summary
Chapter 2 Feature Engineering
2.1 Data Normalization
2.1.1 Min-max Normalization
2.1.2 Standard Normalization
2.2 Data Discretization
2.2.1 Binning
2.2.2 Clustering Analysis
2.2.3 Entropy-based Discretization
2.2.4 Correlation Analysis
2.3 Feature Selection
2.3.1 Filter Feature Selection
2.3.2 Wrapper Feature Selection
2.3.3 Embedded Methods
2.4 Feature Extraction
2.4.1 Principal Components Analysis
2.4.2 Linear Discriminant Analysis
2.4.3 Autoencoder
2.5 Summary
Chapter 3 Instance-Based Learning
3.1 Overview of IBL
3.2 Components of KNN
3.2.1 Measure the Similarity between Instances
3.2.2 How to Choose K
3.2.3 Assign the Class Label
3.2.4 Time Complexity
3.3 Variants of KNN
3.3.1 Attribute Weighted KNN
3.3.2 Distance Weighted KNN
3.4 Strengths and Weaknesses of KNN
Chapter 4 Decision Tree Learning
4.1 Decision Tree Representation
4.1.1 Component of Decision Tree
4.1.2 How to use Decision Trees for Classification?
4.1.3 How to Generate Rules from Decision Trees?
4.1.4 Popular Algorithms to Generate Decision Trees
4.2 ID3 Algorithm
4.2.1 Select the best Attribute
内容摘要
本书以交通数据为对象，以解决交通问题为目标，体现交通工程专业的特点。
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