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An evolutionary approach to constraint-regularized learning.

Eyke Hüllermeier, Ingo Renners, Adolf Grauel (2004)

Mathware and Soft Computing

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to employ fuzzy set-based modeling techniques in order to express such knowledge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn ing process within the regularization framework of inductive...

An Exact Method for Solving the Multi-Processor Flow-Shop

Jacques Carlier, Emmanuel Neron (2010)

RAIRO - Operations Research

The aim of this paper is to present a new branch and bound method for solving the Multi-Processor Flow-Shop. This method is based on the relaxation of the initial problem to m-machine problems corresponding to centers. Release dates and tails are associated with operations and machines. The branching scheme consists in fixing the inputs of a critical center and the lower bounds are those of the m-machine problem. Several techniques for adjusting release dates and tails have also been introduced....

An exercise on Fibonacci representations

Jean Berstel (2001)

RAIRO - Theoretical Informatics and Applications - Informatique Théorique et Applications

We give a partial answer to a question of Carlitz asking for a closed formula for the number of distinct representations of an integer in the Fibonacci base.

An Exercise on Fibonacci Representations

Jean Berstel (2010)

RAIRO - Theoretical Informatics and Applications

We give a partial answer to a question of Carlitz asking for a closed formula for the number of distinct representations of an integer in the Fibonacci base.

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