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Sequential Event Prediction with Association Rules
Published on Aug 02, 20113706 Views
We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on associa
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Sequential Event Prediction with Association Rules00:00
Freshdirect - 100:05
Freshdirect - 200:31
Your cart - 100:33
Your cart - 200:55
Example - 101:12
Example - 201:21
Example - 301:27
Example - 401:43
Top 10 algorithms in data mining02:32
Example - 503:06
Example - 603:48
“Max Confidence, Min Support” Algorithm03:52
About Max Conf, Min Support Algorithm04:54
Try "Adjusted Confidence" Instead - 105:48
“Adjusted Confidence” Algorithm06:13
Try “Adjusted Confidence” Instead - 206:38
This work07:19
Max Conf, Min Supp Algorithm has stronger guarantees than Adjusted Conf Algorithm10:06
Outline of paper - 110:31
Define learning problems11:19
Highest‐scoring‐correct rule and highest‐scoring‐incorrect rule - 112:07
Highest‐scoring‐correct rule and highest‐scoring‐incorrect rule - 213:39
0-1 Loss function - 113:45
0-1 Loss function - 213:49
0-1 Loss function - 313:50
0-1 Loss function - 414:02
Outline of paper - 214:13
Theorem (Large sample, Adj. Conf., Seq. Event Pred.) - 114:46
Theorem (Large sample, Adj. Conf., Seq. Event Pred.) - 215:10
Theorem (Large sample, Adj. Conf., Seq. Event Pred.) - 315:11
Theorem (Large sample, Adj. Conf., Seq. Event Pred.) - 415:37
when K=Kr=0...15:59
Corollary (Large sample, Adj. Conf., Seq. Event Pred.)16:07
Outline of paper - 316:14
Theorem (Small sample, Adj. Conf., Seq. Event Pred.) - 116:18
Theorem (Small sample, Adj. Conf., Seq. Event Pred.) - 216:41
Theorem (Small sample, Adj. Conf., Seq. Event Pred.) - 316:58
Outline of paper - 417:14
Summary of Bounds17:22
Current Work17:54
Thank you18:40