1,816,730 research outputs found
Unsolvability Cores in Classification Problems
Classification problems have been introduced by M. Ziegler as a
generalization of promise problems. In this paper we are concerned with
solvability and unsolvability questions with respect to a given set or language
family, especially with cores of unsolvability. We generalize the results about
unsolvability cores in promise problems to classification problems. Our main
results are a characterization of unsolvability cores via cohesiveness and
existence theorems for such cores in unsolvable classification problems. In
contrast to promise problems we have to strengthen the conditions to assert the
existence of such cores. In general unsolvable classification problems with
more than two components exist, which possess no cores, even if the set family
under consideration satisfies the assumptions which are necessary to prove the
existence of cores in unsolvable promise problems. But, if one of the
components is fixed we can use the results on unsolvability cores in promise
problems, to assert the existence of such cores in general. In this case we
speak of conditional classification problems and conditional cores. The
existence of conditional cores can be related to complexity cores. Using this
connection we can prove for language families, that conditional cores with
recursive components exist, provided that this family admits an uniform
solution for the word problem
Fuzzy heterogeneous neurons for imprecise classification problems
In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.Peer ReviewedPostprint (author's final draft
Graded commutative algebras: examples, classification, open problems
We consider \G-graded commutative algebras, where \G is an abelian group.
Starting from a remarkable example of the classical algebra of quaternions and,
more generally, an arbitrary Clifford algebra, we develop a general viewpoint
on the subject. We then give a recent classification result and formulate an
open problem
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