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Efficient discriminative learning of Bayesian network classifier via Boosted Augmented Naive Bayes
Published on Feb 25, 20076443 Views
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be subo
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Chapter list
Boosted Augmented Naive Bayes Efficient discriminative learning of Bayesian network classifiers 00:00
Contribution 01:18
Bayesian network02:21
Parameter Learning03:06
Model selection03:50
Talk outline05:28
Exponential Loss Function (ELF)06:06
slide806:39
Results: 25 UCI datasets (BNB) 07:18
Results: 25 UCI datasets (BNB) 08:08
Evaluation of BNB08:25
Structure Learning09:26
Creating 10:19
Initial structure10:47
Iteratively adding edges11:10
Final BAN structure11:38
Analysis of BAN12:00
Computational complexity of BAN12:13
Result (simulated dataset):13:03
Results: (simulated dataset):13:30
Results: (simulated dataset):13:47
Results: 25 UCI datasets (BAN) 14:13
Results: BAN vs. Standard method14:33
Results: BAN vs. Structure Learning14:49
Results: BAN vs. ELR15:26
Evaluation of BAN vs. BNB15:50
Conclusion 16:44
Future Work 17:12