Titlebook: Data Mining and Constraint Programming; Foundations of a Cro Christian Bessiere,Luc De Raedt,Dino Pedreschi Book 2016 Springer Internationa

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Advanced Portfolio Techniques problems more efficient, as surveyed in the previous chapter. In this chapter, we take a look at a detailed case study that leverages transformations between problem representations to make portfolios more effective, followed by extensions to the state of the art that make algorithm selection more

一窩小鳥(niǎo)

Adapting Consistency in Constraint Solvingtwo approaches to adjust the level of consistency depending on the instance and on which part of the instance we propagate. The first approach, parameterized local consistency, uses as parameter the . of values, which is a feature computed by arc consistency algorithms during their execution. Parame

小淡水魚(yú)

Modeling in MiningZincage for modeling combinatorial (optimisation) problems. This language is augmented with a library of functions and predicates that help modeling data mining problems and facilities for interfacing with databases. We show how MiningZinc can be used to model constraint-based itemset mining problems, f

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The Inductive Constraint Programming Loope same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the .. In this approach data is gathered and analyzed systematically