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 booklet constitutes the refereed convention complaints of the 14th foreign convention on clever info 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 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 learn contributions that will be thought of initial in different prime meetings and journals, yet that experience a in all probability dramatic effect. To facilitate this, IDA 2015 will function 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

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E. e. closed( A, G)). Difference constraints. Queries Q4 and Q5 both ask for patterns that demonstrate a difference between two probabilistic models. Let PG1 (A) and PG2 (A) be the probability of pattern A according to networks G1 and G2 . The constraint difference( A, G1 , G2 , β) requires that the difference of the probability of a pattern in these two networks is larger than β. In Q4, the two networks are obtained by assigning a variable in the original network to different values (B={Age=Adolescent} and C={Age=Senior} respectively).

The pattern and needs to be marginalized away, so we set ∀k ∈ D(X1 ) : λ1,k = 1. Then, one computes the values of the internal AC nodes bottom-up, according to their operation (× or +). The value of the root node is the requested probability. This can be encoded in CP for arbitrary ACs: for each indicator variable λi,j in the AC, we introduce a Boolean CP variable Bi,j ; the relation between the indicator variables and the CP variables Qi is then modeled by the following constraints (recall that Qi = 0 means variable Xi is not in the pattern): 1 1,1 2,1 3,2 1,1 3,2 Qi = 0 → ∧j (Bi,j = 1) Qi = k → (Bi,k = 1) ∧ (∧j=k (Bi,j = 0)) 1,2 2,2 1,2 2,2 3,1 2,1 3,1 ∀i ∀i, ∀k = 0 We then introduce real-valued variable P , which will represent the computed probability.

A) dist(πlstart ,πd ) < mpick , the shortest path distance between the rider r r intended start and the expected driver path is lower than the maximal distance for the rider’s pick-up. , the shortest path distance between the rider (b) dist(πlstart ,πd ) < mdrop r r intended destination and the expected driver path is lower than the maximal distance for the rider’s drop-off. t. T SD ⊆ T S is the set of drivers’ trip schedules, T SR ⊆ T S is the set of riders’ trip schedules, and every edge (tsd , tsr ) ∈ E is a feasible ride match.

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