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

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

This e-book constitutes the refereed convention lawsuits of the 14th foreign convention on clever information research, which used to be held in October 2015 in Saint Étienne. France. The 29 revised complete papers have been rigorously reviewed and chosen from sixty five submissions. the conventional 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 examine contributions that may be thought of initial in different prime meetings and journals, yet that experience a almost certainly dramatic effect. To facilitate this, IDA 2015 will characteristic tracks: a customary "Proceedings" tune, in addition to a "Horizon" tune for early-stage learn of probably ground-breaking nature.

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Example text

Constraint-Based Querying for Bayesian Network Exploration 21 The variable F1 can be computed as before, while the relative frequency of a database over the same variables can be computed using a constraint programming for itemset mining formulation [9]. In Table 2 we materialize the relative frequency through a CP variable R. As we have shown, many constraints over the pattern and the network can be readily formulated in CP. Furthermore, as these are standard CP constraints, existing CP solvers can be used to enumerate the satisfying BN patterns.

The advantages compared to the above-mentioned methods concern its efficiency and its ability to be applied when few labeled examples are available. It dispenses with the use of validation sets which can be cumbersome to produce in unbalanced or small datasets. It is, however, intended for model selection only, whereas cross-validation and hold-out estimation can be used for performance evaluation as well. 3 Accuracy and Macro-F1 Quantification Bounds In this section, we propose an upper bound on several performance measures (accuracy and macro-F1) of a given classifier C on a dataset S which doesn’t need to be labeled.

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.

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