38,674 research outputs found
A Polyhedral Method to Compute All Affine Solution Sets of Sparse Polynomial Systems
To compute solutions of sparse polynomial systems efficiently we have to
exploit the structure of their Newton polytopes. While the application of
polyhedral methods naturally excludes solutions with zero components, an
irreducible decomposition of a variety is typically understood in affine space,
including also those components with zero coordinates. We present a polyhedral
method to compute all affine solution sets of a polynomial system. The method
enumerates all factors contributing to a generalized permanent. Toric solution
sets are recovered as a special case of this enumeration. For sparse systems as
adjacent 2-by-2 minors our methods scale much better than the techniques from
numerical algebraic geometry
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
Two Decades of Maude
This paper is a tribute to JosĂ© Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
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