On Fuzzy Cardinal Semantic Transformations
- URL: http://arxiv.org/abs/2206.11265v1
- Date: Fri, 27 May 2022 16:17:58 GMT
- Title: On Fuzzy Cardinal Semantic Transformations
- Authors: Alexander Chunikhin, Vadym Zhytniuk
- Abstract summary: fuzzy cardinal semantic transformation as a basis for creating fuzzy semantic numeration systems is introduced in this work.
Both fuzziness of the initial data - cardinals of abstract entities - and fuzziness of the parameters of the cardinal semantic operators are considered.
- Score: 77.34726150561087
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The concept of fuzzy cardinal semantic transformation as a basis for creating
fuzzy semantic numeration systems is introduced in this work. Both fuzziness of
the initial data - cardinals of abstract entities - and fuzziness of the
parameters of the cardinal semantic operators are considered. We also expressed
cardinal semantic transformations for discrete fuzzy numbers and for continuous
triangular fuzzy numbers. The principle of formation of the fuzzy common carry
in the cardinal semantic operators with multiple inputs is formed.
Related papers
- Semantic Numeration Systems as Dynamical Systems [55.2480439325792]
The cardinal abstract object (CAO) formed by them in a certain connectivity topology is proposed to be considered as a linear discrete dynamical system with nonlinear control.<n>The fundamental role of the configuration matrix, which combines information about the types of cardinal semantic operators in the CAO, their parameters and topology of connectivity, is demonstrated.
arXiv Detail & Related papers (2025-07-28T19:29:36Z) - Transfinite Fixed Points in Alpay Algebra as Ordinal Game Equilibria in Dependent Type Theory [0.0]
This paper contributes to the Alpay Algebra by demonstrating that the stable outcome of a self referential process is identical to the unique equilibrium of an unbounded revision dialogue between a system and its environment.<n>By unifying concepts from fixed point theory, game semantics, ordinal analysis, and type theory, this research establishes a broadly accessible yet formally rigorous foundation for reasoning about infinite self referential systems.
arXiv Detail & Related papers (2025-07-25T13:12:55Z) - Relative Representations: Topological and Geometric Perspectives [53.88896255693922]
Relative representations are an established approach to zero-shot model stitching.
We introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations.
Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes.
arXiv Detail & Related papers (2024-09-17T08:09:22Z) - EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention [88.45459681677369]
We propose a novel transformer variant with complex vector attention, named EulerFormer.
It provides a unified theoretical framework to formulate both semantic difference and positional difference.
It is more robust to semantic variations and possesses moresuperior theoretical properties in principle.
arXiv Detail & Related papers (2024-03-26T14:18:43Z) - Mathematical Foundations for a Compositional Account of the Bayesian
Brain [0.0]
We use the tools of contemporary applied category theory to supply functorial semantics for approximate inference.
We define fibrations of statistical games and classify various problems of statistical inference as corresponding sections.
We construct functors which explain the compositional structure of predictive coding neural circuits under the free energy principle.
arXiv Detail & Related papers (2022-12-23T18:58:17Z) - Lattice Generalizations of the Concept of Fuzzy Numbers and Zadeh's
Extension Principle [0.0]
The concept of a fuzzy number is generalized to the case of a finite carrier set of partially ordered elements.
The use of partially ordered values in cognitive maps with comparison of expert assessments is considered.
arXiv Detail & Related papers (2022-08-12T11:32:33Z) - Permutation invariant matrix statistics and computational language tasks [0.7373617024876724]
We introduce a geometry of observable vectors for words, defined by exploiting the graph-theoretic basis for the permutation invariants.
We describe successful applications of this unified framework to a number of tasks in computational linguistics.
arXiv Detail & Related papers (2022-02-14T16:06:29Z) - Fundamentals of Semantic Numeration Systems. Can the Context be
Calculated? [91.3755431537592]
This work is the first to propose the concept of a semantic numeration system (SNS) as a certain class of context-based numeration methods.
The development of the SNS concept required the introduction of fundamentally new concepts.
arXiv Detail & Related papers (2021-01-30T21:54:59Z) - On graded semantics of abstract argumentation: Extension-based case [0.0]
This paper considers some issues on extension-based semantics for abstract argumentation framework (AAF)
An alternative fundamental lemma is given, which generalizes the corresponding result obtained in [1].
A number of fundamental semantics for AAF, including conflict-free, admissible, complete and stable semantics, are shown to be closed under reduced meet modulo an ultrafilter.
arXiv Detail & Related papers (2020-12-19T04:32:19Z) - Compositional Generalization via Semantic Tagging [81.24269148865555]
We propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models.
We show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.
arXiv Detail & Related papers (2020-10-22T15:55:15Z) - Convolutional Ordinal Regression Forest for Image Ordinal Estimation [52.67784321853814]
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation.
The proposed CORF integrates ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.
arXiv Detail & Related papers (2020-08-07T10:41:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.