Knn notes (02) (Phương pháp k láng giềng gần nhất k-NN)
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Bài giảng về phương pháp k láng giềng gần nhất (k-NN), bao gồm giới thiệu, phân loại, hồi quy, lời nguyền chiều, độ phức tạp tính toán và các biến thể.
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STAT 479: Machine Learning Lecture Notes Sebastian Raschka Department of Statistics University of Wisconsin–Madison http://stat.wisc.edu/∼sraschka/teaching/stat479-fs2018/ Fall 2018 Contents 2 Nearest Neighbor Methods 2.1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.1.1 Key concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.1.2 Nearest Neighbor Classification In Context . . . . . . . . . . . . . . . 2 2.1.3 Common Use Cases of k NN . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Nearest Neighbor Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Nearest Neighbor Decision Boundary . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 k -Nearest Neighbor Classification and Regression . . . . . . . . . . . . . . . . 5 2.4.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.4.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Curse of Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.6 Computational Complexity and the Big-O Notation . . . . . . . . . . . . . . 8 2.6.1 2.7 2.8 Big O of k NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Improving Computational Performance . . . . . . . . . . . . . . . . . . . . . . 10 2.7.1 Naive k NN Algorithm in Pseudocode . . . . . . . . . . . . . . . . . . . 10 2.7.2 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.7.3 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.7.4 Faster Distance Metric/Heuristic . . . . . . . . . . . . . . . . . . . . . 12 2.7.5 “Pruning” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.7.6 Parallelizing k NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Distance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.
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- Document name
- Knn notes (02) (Phương pháp k láng giềng gần nhất k-NN)
- School / Course
- University Wisconsin-Madison · Machine learning
- Content
- Tài liệu này trình bày chi tiết về các phương pháp Hàng xóm Gần nhất, đặc biệt là thuật toán k-Nearest Neighbor (k NN). Nó bao gồm giới thiệu, thuật toán, ứng dụng, các vấn đề liên quan như độ phức tạp và cách cải thiện, cùng với các phương pháp đo lường và ưu nhược điểm.
- Table of contents
- 2 Nearest Neighbor Methods
- 2.1 Introduction
- 2.1.1 Key concepts
- 2.1.2 Nearest Neighbor Classification In Context
- 2.1.3 Common Use Cases of k NN
- 2.2 Nearest Neighbor Algorithm
- 2.3 Nearest Neighbor Decision Boundary
- 2.4 k -Nearest Neighbor Classification and Regression
- 2.4.1 Classification
- 2.4.2 Regression
- 2.5 Curse of Dimensionality
- 2.6 Computational Complexity and the Big-O Notation
- 2.6.1 Big O of k NN
- 2.7 Improving Computational Performance
- 2.7.1 Naive k NN Algorithm in Pseudocode
- 2.7.2 Data Structures
- 2.7.3 Dimensionality Reduction
- 2.7.4 Faster Distance Metric/Heuristic
- 2.7.5 “Pruning”
- 2.7.6 Parallelizing k NN
- 2.8 Distance measures
- 2.8.1 Discrete Features
- 2.8.2 Feature Weighting
- 2.9 Distance-weighted k NN
- 2.10 Improving Predictive Performance
- 2.11 Error Bounds
- 2.12 k NN from a Bayesian Perspective
- 2.13 Advantages and Disadvantages of k NN
- 2.14 Other Forms of Instance-based Learning
- 2.14.1 Locally Weighted Regression
- 2.14.2 Kernel Methods
- 2.15 k NN in Python
- 2.16 Resources
- 2.17 Assigned Reading
- 2.18 Further Reading
- Pages
- 23 pages
- Uploaded by
- Uni24h
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Knn notes (02) (Phương pháp k láng giềng gần nhất k-NN)
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STAT 479: Machine Learning Lecture Notes Sebastian Raschka Department of Statistics University of Wisconsin–Madison http://stat.wisc.edu/∼sraschka/teaching/stat479-fs2018/ Fall 2018 Contents 2 Nearest Neighbor Methods 2.1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.1.1 Key concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.1.2 Nearest Neighbor Classification In Context . . . . . . . . . . . . . . . 2 2.1.3 Common Use Cases of k NN . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Nearest Neighbor Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Nearest Neighbor Decision Boundary . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 k -Nearest Neighbor Classification and Regression . . . . . . . . . . . . . . . . 5 2.4.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.4.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Curse of Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.6 Computational Complexity and the Big-O Notation . . . . . . . . . . . . . . 8 2.6.1 2.7 2.8 Big O of k NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Improving Computational Performance . . . . . . . . . . . . . . . . . . . . . . 10 2.7.1 Naive k NN Algorithm in Pseudocode . . . . . . . . . . . . . . . . . . . 10 2.7.2 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.7.3 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.7.4 Faster Distance Metric/Heuristic . . . . . . . . . . . . . . . . . . . . . 12 2.7.5 “Pruning” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.7.6 Parallelizing k NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Distance measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.
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- Document name
- Knn notes (02) (Phương pháp k láng giềng gần nhất k-NN)
- School / Course
- University Wisconsin-Madison · Machine learning
- Content
- Tài liệu này trình bày chi tiết về các phương pháp Hàng xóm Gần nhất, đặc biệt là thuật toán k-Nearest Neighbor (k NN). Nó bao gồm giới thiệu, thuật toán, ứng dụng, các vấn đề liên quan như độ phức tạp và cách cải thiện, cùng với các phương pháp đo lường và ưu nhược điểm.
- Table of contents
- 2 Nearest Neighbor Methods
- 2.1 Introduction
- 2.1.1 Key concepts
- 2.1.2 Nearest Neighbor Classification In Context
- 2.1.3 Common Use Cases of k NN
- 2.2 Nearest Neighbor Algorithm
- 2.3 Nearest Neighbor Decision Boundary
- 2.4 k -Nearest Neighbor Classification and Regression
- 2.4.1 Classification
- 2.4.2 Regression
- 2.5 Curse of Dimensionality
- 2.6 Computational Complexity and the Big-O Notation
- 2.6.1 Big O of k NN
- 2.7 Improving Computational Performance
- 2.7.1 Naive k NN Algorithm in Pseudocode
- 2.7.2 Data Structures
- 2.7.3 Dimensionality Reduction
- 2.7.4 Faster Distance Metric/Heuristic
- 2.7.5 “Pruning”
- 2.7.6 Parallelizing k NN
- 2.8 Distance measures
- 2.8.1 Discrete Features
- 2.8.2 Feature Weighting
- 2.9 Distance-weighted k NN
- 2.10 Improving Predictive Performance
- 2.11 Error Bounds
- 2.12 k NN from a Bayesian Perspective
- 2.13 Advantages and Disadvantages of k NN
- 2.14 Other Forms of Instance-based Learning
- 2.14.1 Locally Weighted Regression
- 2.14.2 Kernel Methods
- 2.15 k NN in Python
- 2.16 Resources
- 2.17 Assigned Reading
- 2.18 Further Reading
- Pages
- 23 pages
- Uploaded by
- Uni24h
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