Results 61 to 70 of about 848,965 (236)
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
, 2018 Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems.
Airola, Antti +4 more
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Advanced Engineering Materials, EarlyView.Creep experiments at 900°C on coarse‐grained steel‐ceramic composites containing recycled magnesia reveal that higher ceramic volume fractions significantly enhance the creep resistance. Detailed EBSD investigations identify subgrain formation in the steel matrix as the dominant deformation mechanism.
Moritz Müller +6 more
wiley +1 more source
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics [PDF]
, 2018 Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information.
Chen, Tianyi +2 more
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Advanced Functional Materials, EarlyView.Biofabrication aims at providing innovative technologies and tools for the fabrication of tissue‐like constructs for tissue engineering and regenerative medicine applications. By integrating multiple biofabrication technologies, such as 3D (bio) printing with fiber fabrication methods, it would be more realistic to reconstruct native tissue's ...
Waseem Kitana +2 more
wiley +1 more source
Operating System’s Kernel Hooking Methods (Study Case of Linux Kernel)
Безопасность информационных технологий, 2014 The article presents an overview of dynamic integration in the kernel Linux, allowed to modify (add, change) its functionality. Traditional methods of integration based on changing in the kernel code (patching), and methods based on using system ...
Ilya Vladimirovich Matveychikov
doaj
Sharp analysis of low-rank kernel matrix approximations [PDF]
, 2013 We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine.
Bach, Francis
core +4 more sources
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 +2 more
core +1 more source
Advanced Functional Materials, EarlyView.The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner +4 more
wiley +1 more source
Locally-Scaled Kernels and Confidence Voting
Machine Learning and Knowledge ExtractionClassification, the task of discerning the class of an unlabeled data point using information from a set of labeled data points, is a well-studied area of machine learning with a variety of approaches.
Elizabeth Hofer, Martin v. Mohrenschildt
doaj +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source