Results 21 to 30 of about 719,028 (211)

Entropy-Based Uncertainty-Aware Exploratory Factor Analysis for Ordinal Data: Application to Tramway Cultural Tourism Evaluation [PDF]

Entropy
Background: Perception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories ...
Jiaozi Pu, Yaxin Shi
doaj   +2 more sources

An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique

Entropy, 2015
In this paper we develop a new procedure for entropic image edge detection. The presented method computes the Jensen–Shannon divergence of the normalized grayscale histogram of a set of multi-sized double sliding windows over the entire image.
José Martínez-Aroza, Qutaibeh Katatbeh
exaly   +3 more sources

Metric character of the quantum Jensen-Shannon divergence [PDF]

Physical Review A, 2008
In a recent paper, the generalization of the Jensen-Shannon divergence in the context of quantum theory has been studied [Majtey et al., Phys. Rev. A 72, 052310 (2005)].
Casas, Montserrat, Lamberti, Pedro W., Borras, A., Plastino, Ángel Luis, Majtey, Ana P.   +4 more
core   +5 more sources

Jensen shannon divergence as reduced reference measure for image denoising

, 2016
This paper focuses on the use the Jensen Shannon divergence for guiding denoising. In particular, it aims at detecting those image regions where noise is masked; denoising is then inhibited where it is useless from the visual point of view. To this aim a
Domenico Vitulano, Vittoria Bruni, BRUNI, VITTORIA, Vitulano, Domenico   +3 more
core   +2 more sources

Evaluating Large Language Models for Decision Support in Minimally Invasive Spine Surgery Triage and Procedural Categories [PDF]

Global Spine Journal
Study Design Vignette-based cross-sectional study. Objective Generative artificial intelligence (AI) programs such as large language models (LLMs) are reshaping treatment decision-making, yet applications in minimally invasive spine surgery (MISS) are ...
Ahmet Kartal MD, Noel F. Manalil MBA, Chiungwen D. Cheng BS, Lawrance K. Chung MD, Harry Gebhard MD, Michael Greenberg BS, Roger Härtl MD, Galal A. Elsayed MD   +7 more
doaj   +2 more sources

Analysis of symbolic sequences using the Jensen-Shannon divergence

Physical Review E, 2002
We study statistical properties of the Jensen-Shannon divergence D, which quantifies the difference between probability distributions, and which has been widely applied to analyses of symbolic sequences.
Stanley, H. E., Grosse, I., Bernaola-Galvan, P., Roman-Roldan, R., Oliver, J., Carpena, P.   +5 more
core   +3 more sources

The Representation Jensen-Shannon Divergence

CoRR, 2023
Quantifying the difference between probability distributions is crucial in machine learning. However, estimating statistical divergences from empirical samples is challenging due to unknown underlying distributions. This work proposes the representation Jensen-Shannon divergence (RJSD), a novel measure inspired by the traditional Jensen-Shannon ...
Jhoan Keider Hoyos-Osorio, Luis G. Sánchez Giraldo   +1 more
openaire   +2 more sources

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

Entropy, 2021
We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius.
Frank Nielsen
doaj   +1 more source

Discrete Versions of Jensen–Fisher, Fisher and Bayes–Fisher Information Measures of Finite Mixture Distributions

Entropy, 2021
In this work, we first consider the discrete version of Fisher information measure and then propose Jensen–Fisher information, to develop some associated results.
Omid Kharazmi, Narayanaswamy Balakrishnan   +1 more
doaj   +1 more source

Quantifying the Dissimilarity of Texts

Information, 2023
Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering.
Benjamin Shade, Eduardo G. Altmann
doaj   +1 more source