Construction of networks by associating with submanifolds of almost Hermitian manifolds
The idea that data lies on a non-linear space has brought up the concept of manifold learning as a part of machine learning.
arXiv subjects
Publications and source records attributed to Arif Gursoy.
The idea that data lies on a non-linear space has brought up the concept of manifold learning as a part of machine learning.
In this paper, we deal with a calculus system SLCD (Syllogistic Logic with Carroll Diagrams), which gives a formal approach to logical reasoning with diagrams, for representations of the fundamental Aristotelian categorical propositions and show that they are closed under the syllogistic criterion of inference which is the deletion of middle term. Therefore, it is implemented to let the formalism comprise synchronically bilateral and trilateral diagrammatical appearance and a naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand as well as to use it. Consequently, we give an effective algorithm used to determine whether a syllogistic reasoning valid or not by using SLCD.