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137,644
A Fixed Point Theorem in Fuzzy Metric Spaces
"... In this paper, we prove a common fixed point theorem by using a new continuity condition in fuzzy metric spaces. This gives an alternative answer of the problem of Rhoades [15]. Our results extend, generalize and fuzzify several fixed point theorems on metric spaces, Menger probabilistic metric spac ..."
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Cited by 29 (0 self)
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In this paper, we prove a common fixed point theorem by using a new continuity condition in fuzzy metric spaces. This gives an alternative answer of the problem of Rhoades [15]. Our results extend, generalize and fuzzify several fixed point theorems on metric spaces, Menger probabilistic metric
Common fixed point theorems in fuzzy metric spaces
 Annals of Fuzzy Mathematics and Informatics
"... Abstract—The aim of this paper is to prove a common fixed theorem for four mappings under weakly compatible condition in fuzzy metric space. While proving our results we utilize the idea of weakly compatible maps due to Jungck and Rhoades. Our results substantially generalize and improve a multitude ..."
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Cited by 3 (2 self)
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Abstract—The aim of this paper is to prove a common fixed theorem for four mappings under weakly compatible condition in fuzzy metric space. While proving our results we utilize the idea of weakly compatible maps due to Jungck and Rhoades. Our results substantially generalize and improve a
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
 Machine Learning
, 1998
"... . This paper addresses the problem of building largescale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximumlikelihood estimation problem. It then devises a practical algorithm for generating the most likely map from ..."
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Cited by 487 (47 self)
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. This paper addresses the problem of building largescale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximumlikelihood estimation problem. It then devises a practical algorithm for generating the most likely map from
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture
The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain
 Psychological Review
, 1958
"... If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what ..."
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Cited by 1143 (0 self)
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If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
The space complexity of approximating the frequency moments
 JOURNAL OF COMPUTER AND SYSTEM SCIENCES
, 1996
"... The frequency moments of a sequence containing mi elements of type i, for 1 ≤ i ≤ n, are the numbers Fk = �n i=1 mki. We consider the space complexity of randomized algorithms that approximate the numbers Fk, when the elements of the sequence are given one by one and cannot be stored. Surprisingly, ..."
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Cited by 855 (12 self)
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The frequency moments of a sequence containing mi elements of type i, for 1 ≤ i ≤ n, are the numbers Fk = �n i=1 mki. We consider the space complexity of randomized algorithms that approximate the numbers Fk, when the elements of the sequence are given one by one and cannot be stored. Surprisingly
Common Fixed Point Theorems for OWC Maps in Symmetric Fuzzy Metric Spaces
"... The purpose of this paper is to study the existence and uniqueness of common fixed point theorems for owc mappings satisfying a generalized mixed contractive condition of integral type in symmetric GVfuzzy metric spaces. The perceptions of implicit relation function and control function has been ut ..."
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The purpose of this paper is to study the existence and uniqueness of common fixed point theorems for owc mappings satisfying a generalized mixed contractive condition of integral type in symmetric GVfuzzy metric spaces. The perceptions of implicit relation function and control function has been
Results 1  10
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137,644