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We present a new learning scheme for solvers of the Constraint Satisfaction Problem (CSP), which is based on learning (general) constraints rather than the generalized no-goods or signed-clauses that were used in the past.
A recent approach based in Maximum Entropy Harmonic Grammar (MaxEnt) makes use of general constraints that putatively capture statistical generalizations across the lexicon, as well as lexical constraints governing the behavior of individual words.
A generality bias is therefore attributed to learners, whereby they privilege general constraints over lexical ones.
In this paper, in theory, checking the general constraints was performed directly on the lattice of closed itemsets and their generators based on partitioning solution set into disjoint equivalence sub-classes.
The general constraints imposed by a small ring are discussed.
This work studies multiobjective optimization (MOP) of nonsmooth functions subject to general constraints.
A genetic algorithm is proposed to locate the critical surface under general conditions with general constraints.
The more general constraints for the investigation of the robustness with respect to parametric uncertainties and measurement noise are shown.
The article also addresses general constraints on any semantic theory, and compares conceptual semantics with other decompositional approaches to meaning.
This paper deals with the optimization of grasping brittle objects with a multi-fingered robot hand under general constraints such as finger deformability and object positioning tolerances.
General constraints are identified, which are not specific for a certain proprietary concept, but depending on the material, concept type, fabrication, assembly and installation procedures and site location.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com