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Supervised Learning Algorithm
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2016-06-24,
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寫筆記
索引
1. Data Mining Essentials – Supervised Learning資料探勘初探 - 監督式學習
2. Data Mining Algorithms資料探勘的演算法
3. Supervised Learning監督式學習
4. Classification Example分類(範例)
5. Vertebrate Classification Example脊椎動物分類(範例)
6. Supervised Learning: The Process
7. A Twitter Example
8. Supervised Learning Algorithm
9. Decision Tree
10. Decision Tree: Example
11. Decision Tree: Example
12. Decision Tree: Example
13. Decision Tree: Example
14. Decision Tree Construction
15. Decision Tree Construction
16. Entropy Example
社群媒體探勘
1. Introduction
1.1
Introduction - Part 1
1.2
Introduction - Part 2
2. Graph Essentials
2.1
Types of Graphs
2.2
Special Graphs and Special Subgraphs
2.3
Graph Algorithms : Graph/Network Traversal Algorithms
3. Network Measures
3.1
Centrality
3.2
Normalization and EigenVector
3.3
Eigenvector Centrality
3.4
Katz Centrality
3.5
PageRank
3.6
Betweenness Centrality
3.7
Closeness Centrality
3.8
Group Centrality
3.9
Transitivity
3.10
Reciprocity
3.11
Status
3.12
Structural Equivalence
3.13
Regular Equivalence
4. Network Models
4.1
Network Models
4.2
Power Law Distribution
4.3
Clustering Coefficient
4.4
Average Path Length
4.5
Random Graphs
4.6
Evolution of Random Graphs
5. Data Mining Essentials
5.1
Data Mining Essentials
5.2
Data
5.3
Data (Part 2)
5.4
Data (Part 3)
5.5
Data (Part 4)
5.6
Supervised Learning Algorithm
5.7
Unsupervised Learning Algorithm
6. Community Analysis
6.1
Community Analysis
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Unsupervised Learning Algorithm
簡介
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Book Draft PDF and Powerpoint Slides available at http://dmml.asu.edu/smm
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