When a potential hedge between alternatives does not reduce the exposure to uncertainty, we say that the decision maker considers these alternatives structurally similar. We offer a novel approach and suggest that structural similarity is subjective and should be different across decision makers. Structural similarity can be recovered through a property of the individual’s preferences referred ...

OBJECTIVE In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved cosine similarity measures of SNSs based on cosine function, including single valued neutrosophic cosine similarity measures and int...

In many machine learning and pattern recognition tasks, there is always a need for appropriate metric functions to measure pairwise distance or similarity between data, where a metric function is a function that defines a distance or similarity between each pair of elements of a set. In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric fr...

in  cite{gl}, b. gerla and i. leuc{s}tean introduced the notion of similarity on mv-algebra. a similarity mv-algebra is an mv-algebra endowed with a binary operation $s$ that verifies certain additional properties. also, chirtec{s} in cite{c}, study the notion of similarity on l ukasiewicz-moisil algebras. in particular, strong similarity l ukasiewicz-moisil algebras were defined. in this paper...

For q-ary n-sequences, we develop the concept of similarity functions that can be used (for q = 4) to model a thermodynamic similarity on DNA sequences. A similarity function is identified by the length of a longest common subsequence between two q-ary n-sequences. Codes based on similarity functions are called DNA codes [1, 2, 3]. DNA codes are important components in biomolecular computing [4...

For -ary -sequences, we develop the concept of similarity functions that can be used (for ) to model a thermodynamic similarity on DNA sequences. A similarity function is identified by the length of a longest common subsequence between two -ary -sequences. Codes based on similarity functions are called DNA codes [10]. DNA codes are important components in biomolecular computing and other biotec...

This paper describes SODA a novel ontology alignment method for the OWL-DL format. SODA uses a new approach that consists in computing local and semantic similarities among ontological elements. 1 Presentation of the system SODA [1] (Structural Ontology OWL-DL [2] Alignment), is a new approach that aligns two OWL-DL ontologies using similarity measures [3]. Both OWL-DL ontologies are transforme...

Indexing methods have been widely used for fast data retrieval on large scale datasets. When the data are represented by high dimensional vectors, hashing is often used as an efficient solution for approximate similarity search. When a retrieval task does not involve supervised training data, most hashing methods aim at preserving data similarity defined by a distance metric on the feature vect...

Most existing methods for partially occluded object recognition are only suit for Euclid and similarity transformation. As a result, the performance would be degraded in the affine and perspective transformation. This paper focuses on partially occluded shape recognition under affine transformation. The recognition algorithms are as follows : First, a new local invariant under affine transforma...

Existing data mining techniques mostly focus on finding global patterns and lack the ability to systematically discover regional patterns. Most relationships in spatial datasets are regional; therefore there is a great need to extract regional knowledge from spatial datasets. This paper proposes a novel framework to discover interesting regions characterized by “strong regional correlation rela...