Multiple kernel learning for visual object recognition: A review
Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels
for a given recognition task. A number of studies have shown that MKL is a useful tool for …
for a given recognition task. A number of studies have shown that MKL is a useful tool for …
The effects of regularization and data augmentation are class dependent
R Balestriero, L Bottou… - Advances in Neural …, 2022 - proceedings.neurips.cc
Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …
generalization performances by constraining a model's complexity. Current Deep Networks …
[HTML][HTML] On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Understanding and interpreting classification decisions of automated image classification
systems is of high value in many applications, as it allows to verify the reasoning of the …
systems is of high value in many applications, as it allows to verify the reasoning of the …
Multiple Kernel -Means with Incomplete Kernels
Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base
kernel matrices to improve clustering performance. However, existing MKC algorithms …
kernel matrices to improve clustering performance. However, existing MKC algorithms …
Alzheimer's disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion
Alzheimer's disease (AD) will become a global burden in the coming decades according to
the latest statistical survey. How to effectively detect AD or MCI (mild cognitive impairment) …
the latest statistical survey. How to effectively detect AD or MCI (mild cognitive impairment) …
Multiview subspace clustering via co-training robust data representation
Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …
[PDF][PDF] Multiple kernel learning algorithms
M Gönen, E Alpaydın - The Journal of Machine Learning Research, 2011 - jmlr.org
In recent years, several methods have been proposed to combine multiple kernels instead of
using a single one. These different kernels may correspond to using different notions of …
using a single one. These different kernels may correspond to using different notions of …
Learning noise transition matrix from only noisy labels via total variation regularization
Many weakly supervised classification methods employ a noise transition matrix to capture
the class-conditional label corruption. To estimate the transition matrix from noisy data …
the class-conditional label corruption. To estimate the transition matrix from noisy data …
Multiple Kernel k-Means Clustering with Matrix-Induced Regularization
Multiple kernel k-means (MKKM) clustering aims to optimally combine a group of pre-
specified kernels to improve clustering performance. However, we observe that existing …
specified kernels to improve clustering performance. However, we observe that existing …