Results 51 to 60 of about 845,859 (278)

Kernel methods for detecting coherent structures in dynamical data

, 2019
We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems.
Husic, Brooke E., Klus, Stefan, Mollenhauer, Mattes, Noé, Frank   +3 more
core   +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

Positive Definite Kernels in Machine Learning [PDF]

, 2009
This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods.
Cuturi, Marco
core   +1 more source

Serum Uric Acid Levels in Older Adults: Associations With Clinical Outcomes and Implications for Reference Intervals in Those Aged 70 Years and Over

Arthritis Care &Research, EarlyView.
Objective Reports have linked both high and low serum uric acid (SUA) levels to adverse health outcomes. This study aimed to establish a reference interval for SUA in older adults and assessed its association with clinically relevant outcomes in relatively healthy, community‐dwelling individuals aged ≥70 years old.
Amanda J. Rickard, Cammie Tran, Hans G. Schneider, Flavia M. Cicuttini, Anita E. Wluka, Ego Seeman, Johannes T. Neumann, Md Nazmul Karim, Zhen Zhou, Sultana Monira Hussain, David P. Q. Clark, Daniel Clayton‐Chubb, Andrew M. Tonkin, Lawrence J. Beilin, Robyn L. Woods, John J. McNeil   +15 more
wiley   +1 more source

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

, 2014
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad ...
A. Ben-Hur, B. Schölkopf, C.D. Martin, C.F. Loan Van, F. Bauer, I. Steinwart, L. Baldassarre, L. Jacob, L. Lo Gerfo, M. Gönen, M. Schrynemackers, M. Álvarez, M.I. Davis, R. Raymond, S. Oyama, S.J. Pan, T. Pahikkala, T. Pahikkala, T. Pahikkala, T. Pahikkala, W. Waegeman, Y. Koren, Y. Park   +22 more
core   +1 more source

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

Bayesian kernel-based system identification with quantized output data

, 2015
In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which ...
Bottegal, Giulio, Hjalmarsson, Håkan, Pillonetto, Gianluigi   +2 more
core   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

A Fast Neural Network Learning Algorithm with Approximate Singular Value Decomposition

International Journal of Applied Mathematics and Computer Science, 2019
The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms.
Jankowski Norbert, Linowiecki Rafał
doaj   +1 more source

Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests

Statistical Theory and Related Fields, 2022
Many of the best predictors for complex problems are typically regarded as hard to interpret physically. These include kernel methods, Shtarkov solutions, and random forests.
T. M. Le, Bertrand Clarke
doaj   +1 more source