en
0.25
0.5
0.75
1.25
1.5
1.75
2
Learning the Parameters of Probabilistic Logic Programs from Interpretations
Published on Nov 30, 20112815 Views
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilis
Related categories
Chapter list
Learning the Parameters of Probabilistic Logic Programs from Interpretations00:00
Outline00:21
Consider you are ambulance dispatcher...00:40
Probabilistic Programming - 101:09
Probabilistic Programming - 202:20
Probabilistic Programming - 302:41
Probabilistic Programming - 402:48
Probabilistic Programming - 502:59
Probabilistic Programming - 603:00
Training Example03:12
The ILP View on Learning03:44
The ILP View on the Alphabet Soup - 104:19
The ILP View on the Alphabet Soup - 205:06
Contribution of this Work05:17
Toy Example - 106:09
Toy Example - 206:28
Toy Example - 306:38
Toy Example - 406:40
Toy Example - 506:47
Toy Example - 606:49
Toy Example - 707:17
Toy Example - 807:33
Toy Example - 907:41
Sampling - 107:51
Sampling - 207:54
Sampling - 308:10
Parameter Estimation - 108:16
Parameter Estimation - 208:18
Parameter Estimation - 308:31
Missing Values08:34
LFI ProbLog - 109:24
LFI ProbLog - 209:53
Conversion to Probabilistic CNF - 110:27
Conversion to Probabilistic CNF - 211:18
Conversion to Probabilistic CNF - 311:29
Conversion to Probabilistic CNF - 411:54
Conversion to Probabilistic CNF - 512:53
Conversion to Probabilistic CNF - 613:03
Conversion to Probabilistic CNF - 713:13
Conversion to Probabilistic CNF - 813:29
LFI ProbLog - 113:56
LFI ProbLog - 214:02
EM on CNFs - 114:19
EM on CNFs - 214:30
Experimental Results - 114:52
Experimental Results - 215:28
Experimental Results: WebKB - 115:30
Experimental Results: WebKB - 216:09
Experimental Results: Smokers16:57
Conclusions - 117:26
Picture19:45