A dissimilarity function for geospatial polygons

2013 ◽  
Vol 41 (1) ◽  
pp. 153-188 ◽  
Author(s):  
Deepti Joshi ◽  
Leen-Kiat Soh ◽  
Ashok Samal ◽  
Jing Zhang
Keyword(s):  
Dissimilarity Function

Width-based distance measures on interval-valued intuitionistic fuzzy sets

2021 ◽  
pp. 1-13
Author(s):  
Xi Li ◽  
Chunfeng Suo ◽  
Yongming Li
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Dissimilarity Function ◽  
Better Than

An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.

scholarly journals Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function

2012 ◽  
Vol 16 (3) ◽  
pp. 487-511 ◽  
Author(s):  
Siti Sakira Kamaruddin ◽  
Abdul Razak Hamdan ◽  
Azuraliza Abu Bakar ◽  
Fauzias Mat Nor
Keyword(s):  
Error Tolerance ◽  
Conceptual Graph ◽  
Interchange Format ◽  
Dissimilarity Function

Upper-bound estimates for classifiers based on a dissimilarity function

2012 ◽  
Vol 48 (4) ◽  
pp. 592-600 ◽  
Author(s):  
B. P. Rusyn ◽  
V. A. Tayanov ◽  
O. A. Lutsyka
Keyword(s):  
Upper Bound ◽  
Dissimilarity Function

scholarly journals Path Clustering Based on a Novel Dissimilarity Function for Ride-Sharing Recommenders

2016 ◽  
Author(s):  
Eleonora D'Andrea ◽  
David Di Lorenzo ◽  
Beatrice Lazzerini ◽  
Francesco Marcelloni ◽  
Fabio Schoen
Keyword(s):  
Ride Sharing ◽  
Dissimilarity Function

Theory and Algorithm for Learning with Dissimilarity Functions

2009 ◽  
Vol 21 (5) ◽  
pp. 1459-1484 ◽  
Author(s):  
Liwei Wang ◽  
Masashi Sugiyama ◽  
Cheng Yang ◽  
Kohei Hatano ◽  
Jufu Feng
Keyword(s):  
Convex Combination ◽  
Sufficient Conditions ◽  
Learning Paradigm ◽  
Large Margin ◽  
Dissimilarity Measures ◽  
Feature Vectors ◽  
Practical Algorithm ◽  
Dissimilarity Function

We study the problem of classification when only a dissimilarity function between objects is accessible. That is, data samples are represented not by feature vectors but in terms of their pairwise dissimilarities. We establish sufficient conditions for dissimilarity functions to allow building accurate classifiers. The theory immediately suggests a learning paradigm: construct an ensemble of simple classifiers, each depending on a pair of examples; then find a convex combination of them to achieve a large margin. We next develop a practical algorithm referred to as dissimilarity-based boosting (DBoost) for learning with dissimilarity functions under theoretical guidance. Experiments on a variety of databases demonstrate that the DBoost algorithm is promising for several dissimilarity measures widely used in practice.

scholarly journals ToFU: Topology functional units for deep learning

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Christopher Oballe ◽  
David Boothe ◽  
Piotr J. Franaszczuk ◽  
Vasileios Maroulas
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spectral Features ◽  
Learning Tasks ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Persistence Diagram ◽  
Topology Of Data ◽  
Dissimilarity Function

<p style='text-indent:20px;'>We propose ToFU, a new trainable neural network unit with a persistence diagram dissimilarity function as its activation. Since persistence diagrams are topological summaries of structures, this new activation measures and learns the topology of data to leverage it in machine learning tasks. We showcase the utility of ToFU in two experiments: one involving the classification of discrete-time autoregressive signals, and another involving a variational autoencoder. In the former, ToFU yields competitive results with networks that use spectral features while outperforming CNN architectures. In the latter, ToFU produces topologically-interpretable latent space representations of inputs without sacrificing reconstruction fidelity.</p>

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