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

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

This publication constitutes the refereed convention court cases of the 14th overseas 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 normal concentration of the IDA symposium sequence is on end-to-end clever help for facts 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 probably dramatic influence. To facilitate this, IDA 2015 will characteristic tracks: a customary "Proceedings" tune, in addition to a "Horizon" tune for early-stage examine of doubtless ground-breaking nature.

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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.

We denote the set of all patterns of PG by I. A BN pattern query Q is a tuple (PG , C) where C : I → {0, 1} is a conjunction of constraints over a pattern. Pattern A is a solution for Q if C(A) = 1. The result of a query consists of all patterns that satisfy the constraints. The queries used in the examples in Sect. 2 are given in Table 1. Most constraints have close counterparts in the constraint-based pattern mining literature. The main difference is that the notion of (relative) frequency of a pattern in a database is replaced by the probability of the pattern in the BN.

Res. fr 2 Viseo R&D, Grenoble, France Max-Planck Institute for Intelligent Systems, T¨ ubingen, Germany Abstract. Hyper-parameter tuning is a resource-intensive task when optimizing classification models. The commonly used k-fold cross validation can become intractable in large scale settings when a classifier has to learn billions of parameters. At the same time, in real-world, one often encounters multi-class classification scenarios with only a few labeled examples; model selection approaches often offer little improvement in such cases and the default values of learners are used.

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