By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen
This ebook constitutes the refereed convention court cases of the 14th overseas convention on clever facts research, which used to be held in October 2015 in Saint Étienne. France. The 29 revised complete papers have been conscientiously reviewed and chosen from sixty five submissions. the normal concentration of the IDA symposium sequence is on end-to-end clever help for information research. The symposium goals to supply a discussion board for uplifting learn contributions that may be thought of initial in different major meetings and journals, yet that experience a almost certainly dramatic impression. To facilitate this, IDA 2015 will characteristic tracks: a typical "Proceedings" tune, in addition to a "Horizon" tune for early-stage study of probably ground-breaking nature.
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Additional info for Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne, France, October 22–24, 2015, Proceedings
Such an approach supports a wide range of queries and constraints in a ﬂexible and declarative manner. Our work currently focusses on enumeration queries, as is typical in pattern mining. However, it could also be used in an optimisation setting over a scoring function, where its generality would allow one to add arbitrary constraints on top of the scoring function. In future work, the approach could also be adapted to problems beyond enumerating BN pattern queries, such as verifying monotonicity of Bayesian networks  or computing same-decision probability .
In  each explanation found by a K-MPE algorithm is simpliﬁed by removing assignments Constraint-Based Querying for Bayesian Network Exploration 23 that are considered irrelevant;  makes a trade-oﬀ between high probability and speciﬁcity.  proposes a deﬁnition for relevance and gives an algorithm that excludes irrelevant variables from the MAP assignments. This is a speciﬁc optimization query which is solved by a best-ﬁrst-search algorithm. Related to discriminating a BN network from a database, in  the authors search for subsets of variables rather than partial assignments.
To assess the potential of the ride-sharing scheme, we need to infer the parameters of the model from the set of positive examples. For a trip schedule tsu , inferring the time window twu involves inferring the and the latest arrival time tlatest in which a user expect earliest start time tearly u u the journey to happen. For this purpose, we extract from the trip records three parameters: the maximal positive start time delay, δ + , the maximal negative start time delay, δ − , and the estimated travel time f1 and, as explained in Fig.
Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne, France, October 22–24, 2015, Proceedings by Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen