Automatic Learning of Fuzzy Relations

1983 ◽  
pp. 400-415
Author(s):  
Renato De Mori
Keyword(s):  
Fuzzy Relations ◽  
Automatic Learning

Identifying lake longer-term dynamics via fuzzy relations and non-linear Kalman filters

1999 ◽  
Author(s):  
Tatjana D. Kolemishevska-Gugulovska ◽  
Georgi M. Dimirovski ◽  
A. Talha Dinibutun ◽  
Norman E. Gough
Keyword(s):  
Kalman Filters ◽  
Fuzzy Relations ◽  
Non Linear

On the Automatic Learning of Sentiment Lexicons

2015 ◽  
Author(s):  
Aliaksei Severyn ◽  
Alessandro Moschitti
Keyword(s):  
Automatic Learning

scholarly journals Complex T-Spherical Fuzzy Relations with Their Applications in Economic Relationships and International Trades

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Abdul Nasir ◽  
Naeem Jan ◽  
Miin-Shen Yang ◽  
Sami Ullah Khan
Keyword(s):  
Fuzzy Relations ◽  
Economic Relationships

ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases

2021 ◽  
Author(s):  
Jinze Bai ◽  
Jialin Wang ◽  
Zhao Li ◽  
Donghui Ding ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Relational Databases ◽  
Automatic Learning

scholarly journals Aggregation of Indistinguishability Fuzzy Relations Revisited

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1441
Author(s):  
Juan-De-Dios González-Hedström ◽  
Juan-José Miñana ◽  
Oscar Valero
Keyword(s):  
Equivalence Relation ◽  
Fuzzy Relations ◽  
Measure Of Similarity ◽  
Dual Notion ◽  
The Relationship

Indistinguishability fuzzy relations were introduced with the aim of providing a fuzzy notion of equivalence relation. Many works have explored their relation to metrics, since they can be interpreted as a kind of measure of similarity and this is, in fact, a dual notion to dissimilarity. Moreover, the problem of how to construct new indistinguishability fuzzy relations by means of aggregation has been explored in the literature. In this paper, we provide new characterizations of those functions that allow us to merge a collection of indistinguishability fuzzy relations into a new one in terms of triangular triplets and, in addition, we explore the relationship between such functions and those that aggregate extended pseudo-metrics, which are the natural distances associated to indistinguishability fuzzy relations. Our new results extend some already known characterizations which involve only bounded pseudo-metrics. In addition, we provide a completely new description of those indistinguishability fuzzy relations that separate points, and we show that both differ a lot.

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

MEDUSA: A Fuzzy Expert System for Medical Diagnosis of Acute Abdominal Pain

1994 ◽  
Vol 33 (05) ◽  
pp. 522-529 ◽  
Author(s):  
M. Fathi-Torbaghan ◽  
D. Meyer
Keyword(s):  
Fuzzy Logic ◽  
Abdominal Pain ◽  
Expert System ◽  
Acute Abdominal Pain ◽  
Medical Knowledge ◽  
Clinical Problem ◽  
Fuzzy Relations ◽  
Diagnostic Decision ◽  
Special Cases ◽  
Hybrid Concept

Abstract:Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rulebased, heuristic and casebased reasoning on the basis of imprecise information. The central idea of the integration is to use casebased reasoning for the management of special cases, and rulebased reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.

On Convexity of Fuzzy Sets and Fuzzy Relations

1992 ◽  
Vol 59 (1-2) ◽  
pp. 91-102
Author(s):  
Xu Chen-Wei
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Relations
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