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Learning the Parameters of Probabilistic Logic Programs from Interpretations
Published on 2011-11-302820 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
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Presentation
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
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