Results 41 to 50 of about 853,208 (277)

Integrating Distance Correlation and Adaptive Weighting with RBF Kernel Transformations: A Novel Feature Selection Framework with Application to ECG Arrhythmia Detection

Bioengineering
Accurate feature selection is critical for machine learning in medical diagnosis, yet conventional methods often fail to capture complex non-linear relationships in biomedical data.
Monica Fira, Lucian Fira
doaj   +1 more source

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations

Entropy, 2020
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data.
Alessio Martino, Enrico De Santis, Alessandro Giuliani, Antonello Rizzi   +3 more
doaj   +1 more source

Kernel Block Diagonal Representation Subspace Clustering with Similarity Preservation

Applied Sciences, 2023
Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively,
Yifang Yang, Fei Li
doaj   +1 more source

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) [PDF]

, 2015
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs).
Nickisch, Hannes, Wilson, Andrew Gordon
core   +2 more sources

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

, 2018
Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than ...
Kang, Zhao, Lu, Xiao, Xu, Zenglin, Yi, Jinfeng   +3 more
core   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Iraqi Journal for Computer Science and Mathematics, 2023
Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables.
Zakariya Algamal, Firas AL-Taie, Omar Qasim   +2 more
doaj   +1 more source

Fluid Biomarkers of Disease Burden and Cognitive Dysfunction in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Identifying objective biomarkers for progressive supranuclear palsy (PSP) is crucial to improving diagnosis and establishing clinical trial and treatment endpoints. This study evaluated fluid biomarkers in PSP versus controls and their associations with regional 18F‐PI‐2620 tau‐PET, clinical, and cognitive outcomes.
Roxane Dilcher, Charles B. Malpas, Stuart J. McDonald, William T. O’Brien, Craig Despott, Kelly L. Bertram, Matthew P. Pase, Lukas Frontzkowski, Meng Law, Terence J. O’Brien, Lucy Vivash   +10 more
wiley   +1 more source

Kernel Mean Shrinkage Estimators [PDF]

, 2016
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern ...
Fukumizu, Kenji, Gretton, Arthur, Muandet, Krikamol, Schölkopf, Bernhard, Sriperumbudur, Bharath   +4 more
core   +2 more sources

Relative Comparison Kernel Learning with Auxiliary Kernels

, 2014
In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans.
A. Rakotomamonjy, B. McFee, F. Wang, G. Lanckriet, K. Huang, M. Filippone, M. Gönen, M. Kloft, N. Schwertman, R. Shepard   +9 more
core   +1 more source