Multiple kernel clustering with late fusion consensus local graph preserving
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22596
Abstract: Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility;… read more here.
Keywords: local graph; kernel clustering; multiple kernel; graph ... See more keywords
Parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions and its application in fault diagnosis
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3792-7
Abstract: A new model parameter selection method for support vector machine based on adaptive fusion of multiple kernel functions is proposed in this paper. Characteristics of local kernels, global kernels, mixtures of kernels and multiple kernels… read more here.
Keywords: vector; multiple kernel; selection; fusion ... See more keywords
Robust Human Tracking Based on DPM Constrained Multiple-Kernel from a Moving Camera
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-015-1097-y
Abstract: In this paper, we attempt to solve the challenging task of precise and robust human tracking from a moving camera. We propose an innovative human tracking approach, which efficiently integrates the deformable part model (DPM)… read more here.
Keywords: robust human; part; multiple kernel; moving camera ... See more keywords
Multiple Kernel Based Remote Sensing Vegetation Classifier with Levy Optimized Subspace
Sign Up to like & getrecommendations! Published in 2020 at "Wireless Personal Communications"
DOI: 10.1007/s11277-019-06863-4
Abstract: Vegetation classification in remote sensing (RS) applications details a rich source of information on land use/land cover decision making. To classify the mixed land-cover area of diverse categories through RS imagery, robust classification methods and… read more here.
Keywords: optimized subspace; multiple kernel; levy optimized; remote sensing ... See more keywords
Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.09.117
Abstract: Abstract The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with… read more here.
Keywords: multiple kernel; analysis; multimodal sentiment; kernel learning ... See more keywords
Multilayer deep features with multiple kernel learning for action recognition
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.096
Abstract: Abstract In accurate action recognition, discriminative human-region representation as auxiliary information is critical for fusing multiple visual clues in a video and further improving the recognition performance. To this end, in this paper we propose… read more here.
Keywords: action recognition; multilayer deep; multiple kernel; recognition ... See more keywords
DWS-MKL: Depth-width-scaling multiple kernel learning for data classification
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.039
Abstract: Abstract Deep learning technologies have been rapidly developing recently. They have shown excellent performances in many fields. However, deep learning networks have weak adaptability to small sample sizes. Usually, deep learning networks require tens of… read more here.
Keywords: classification; multiple kernel; deep learning; kernel learning ... See more keywords
Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model
Sign Up to like & getrecommendations! Published in 2019 at "NeuroImage"
DOI: 10.1016/j.neuroimage.2018.09.054
Abstract: &NA; Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting‐state functional magnetic resonance imaging (rsfMRI). The resting state… read more here.
Keywords: state; multiple kernel; kernel learning; model ... See more keywords
A classifier for multi-dimensional datasets based on Bayesian multiple kernel grouping learning
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2019.1612395
Abstract: ABSTRACT This paper proposes an algorithm for the classification of multi-dimensional datasets based on the conjugate Bayesian Multiple Kernel Grouping Learning (BMKGL). Using conjugate Bayesian framework improves the computation efficiency. Multiple kernels instead of a… read more here.
Keywords: datasets based; kernel; dimensional datasets; multiple kernel ... See more keywords
Identification of drug-target interactions via multiple kernel-based triple collaborative matrix factorization
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbab582
Abstract: Targeted drugs have been applied to the treatment of cancer on a large scale, and some patients have certain therapeutic effects. It is a time-consuming task to detect drug-target interactions (DTIs) through biochemical experiments. At… read more here.
Keywords: matrix; target interactions; drug target; multiple kernel ... See more keywords
Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3140277
Abstract: Conventional machine learning has paved the way for a simple, affordable, non-invasive approach for Coronary artery disease (CAD) detection using phonocardiogram (PCG). It leaves a scope to explore improvement of performance metrics by fusion of… read more here.
Keywords: kernel learning; multiple kernel; artery disease; detection ... See more keywords