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Lecture 25 : Support Vector Machine IV
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Classroom Contents
Data Mining
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- 1 Lecture 1 Introduction, Knowledge Discovery Process
- 2 Lecture 2 Data Preprocessing - I
- 3 Lecture 3 Data Preprocessing - II
- 4 Lecture 4 Association Rules
- 5 Lecture 5 Apriori algorithm
- 6 Lecture 6 : Rule generation
- 7 Lecture 7 : Classification
- 8 Lecture 8 : Decision Tree - I
- 9 Lecture 9 : Decision Tree - II
- 10 Lecture 10 : Decision Tree III
- 11 Lecture 11 : Decision Tree IV
- 12 Lecture 12 : Bayes Classifier I
- 13 Lecture 13 : Bayes Classifier II
- 14 Lecture 14 : Bayes Classifier III
- 15 Lecture 15 : Bayes Classifier IV
- 16 Lecture 16 : Bayes Classifier V
- 17 Lecture 17 : K Nearest Neighbor I
- 18 Lecture 18 : K Nearest Neighbor II
- 19 Lecture 19 :
- 20 Lecture 20
- 21 Lecture 21
- 22 Lecture 22 : Support Vector Machine I
- 23 Lecture 23 : Support Vector Machine II
- 24 Lecture 24 : Support Vector Machine III
- 25 Lecture 25 : Support Vector Machine IV
- 26 Lecture 26 : Support Vector Machine V
- 27 Lecture 27: Kernel Machines
- 28 Lecture 28: Artificial Neural Networks I
- 29 Lecture 29:Artificial Neural Networks II
- 30 Lecture 30: Artificial Neural Networks III
- 31 Lecture 31: Artificial Neural Networks IV
- 32 Lecture 32: Clustering I
- 33 Lecture 33: Clustering II
- 34 Lecture 34: Clustering III
- 35 Lecture 35: Clustering IV
- 36 Lecture 36: Clustering V
