Results 71 to 80 of about 3,294 (168)
Towards explaining the speed of $k$-means [PDF]
, 2011 The $k$-means method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worst-case running-time. To explain the speed of the $k$-means method, a smoothed analysis has been conducted.
Manthey, Bodo
core +2 more sources
Bayesian influence diagnostics using normalized functional Bregman divergence
Communications in Statistics - Theory and Methods, 2020 Ideally, any statistical inference should be robust to local influences. Although there are simple ways to check about leverage points in independent and linear problems, more complex models require more sophisticated methods. Kullback-Leiber and Bregman divergences were already applied in Bayesian inference to measure the isolated impact of each ...
Ian M. Danilevicz, Ricardo S. Ehlers
openaire +2 more sources
Alpha‐synuclein co‐pathology in a real‐world early Alzheimer's disease cohort
Alzheimer's &Dementia, Volume 22, Issue 3, March 2026.Abstract BACKGROUND Most Alzheimer's disease (AD) cases show mixed pathology, with α‐synuclein (αSyn) aggregates present in a substantial proportion. The cerebrospinal fluid (CSF) α‐synuclein seed amplification assay (αS‐SAA) enables in vivo detection of pathogenic αSyn aggregates, but its clinical significance remains unclear. METHODS We prospectively
Tamara Shiner +15 more
wiley +1 more source
Divergence Network: Graphical calculation method of divergence functions
, 2018 In this paper, we introduce directed networks called `divergence network' in order to perform graphical calculation of divergence functions. By using the divergence networks, we can easily understand the geometric meaning of calculation results and grasp
Nishiyama, Tomohiro
core +1 more source
T‐calibration in semi‐parametric models
Canadian Journal of Statistics, Volume 54, Issue 1, March 2026.AbstractThis article relates the calibration of models to the consistent loss functions for the target functional of the model. Correctly specified models are calibrated. Conversely, we demonstrate that if there is a parameter value that is optimal under all consistent loss functions, then a model is calibrated.
Anja Mühlemann, Johanna Ziegel
wiley +1 more source
Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.This systematic review of 352 studies establishes a comprehensive taxonomy for Interpretable Machine Learning, transitioning from foundational intrinsic models to advanced deep learning explanations. It reveals a critical paradigm shift toward “mechanistic interpretability” and actionable recourse, emphasizing that future AI systems must be rigorously ...
Shimon Fridkin, Michael Bendersky
wiley +1 more source
A new self-organizing neural gas model based on Bregman divergences [PDF]
, 2018 In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in ...
Luque-Baena, Rafael Marcos +3 more
core
Clinical Transplantation, Volume 40, Issue 2, February 2026.ABSTRACT Introduction There are no effective therapeutic agents for preventing or treating delayed graft function (DGF) among deceased donor kidney transplant recipients (DDKTRs). Donor and recipient factors are important to predicting DGF and associated outcomes, which we hypothesize differed over time.
Camille C. Ylagan +7 more
wiley +1 more source
Total Jensen divergences: Definition, Properties and k-Means++ Clustering
, 2013 We present a novel class of divergences induced by a smooth convex function called total Jensen divergences. Those total Jensen divergences are invariant by construction to rotations, a feature yielding regularization of ordinary Jensen divergences by a ...
Nielsen, Frank, Nock, Richard
core
Bregman-Divergence-Based Arimoto-Blahut Algorithm
IEEE Transactions on Information TheoryWe generalize the generalized Arimoto-Blahut algorithm to a general function defined over Bregman-divergence system. In existing methods, when linear constraints are imposed, each iteration needs to solve a convex minimization. Exploiting our obtained algorithm, we propose a minimization-free-iteration algorithm.
openaire +2 more sources