Download PDF by Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen: Advances in Intelligent Data Analysis XIV: 14th

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

ISBN-10: 3319244647

ISBN-13: 9783319244648

ISBN-10: 3319244655

ISBN-13: 9783319244655

This e-book constitutes the refereed convention lawsuits of the 14th overseas convention on clever facts research, which was once 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 aid for info research. The symposium goals to supply a discussion board for uplifting study contributions that would be thought of initial in different top meetings and journals, yet that experience a very likely dramatic impression. To facilitate this, IDA 2015 will characteristic tracks: a customary "Proceedings" music, in addition to a "Horizon" song for early-stage examine of doubtless ground-breaking nature.

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Extra resources for Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne, France, October 22–24, 2015, Proceedings

Example text

Positive and negative time delay for riders in region 4 120 140 8 V. Armant et al. The estimated travel time represents the approximate time a user can expect to spend on the road. In Fig. 4 (left) we plot the duration of the riders’ travel time to the reported drop-off tdrop against the minimal from the reported pick-up tpick r r path time computed between these locations. 5 for the increase in travel time over the minimal path. This increase may be due to many factors, including traffic congestion and the presence of multiple passengers in a single trip, and will be the subject of further study.

The constants assign their corresponding Fv variable to a fixed value. For the indicator variables λi,j , the corresponding Fv variables are channeled to their Boolean counterparts Bi,j meaning they must take the same value (either 0 or 1). The internal nodes are then simply encoded by their operation, namely constraint Fv = w∈Ch(v) Fw for product nodes and constraint 20 B. Babaki et al. Fv = w∈Ch(v) Fw for sum nodes, where Ch(v) are the identifiers of the children of node v in the AC. Because of these constraints, when all Qi (and hence Bi ) variables are assigned, each Fv represents the value of that node of the AC, and the root node F1 is the probability of the BN pattern.

In: ECAI 2014–21st European Conference on Artificial Intelligence, 18–22 August 2014, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014), pp. 831–836 (2014) 13. : Ride-Sharing: a multi sourcedestination path planning approach. , Zhang, D. ) AI 2012. LNCS, vol. 7691, pp. 815–826. be Universiteit Leiden, Niels Bohrweg 1, 2333 Ca Leiden, The Netherlands Abstract. Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large network is to be used for the first time, or when the network is complex or has just been updated.

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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

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