Multiple kernel learning Multiple kernel learning (MKL) is a set of machine learn- ing methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of multiple kernels. It can be applied to select for an optimal kernel and parameters, and combine ...
To identify relevant studies for this review, Web of Science, Scopus and PubMed were searched for original research papers published between January 2015 and July 2024 with the following keywords: “machine learning” OR “artificial intelligence” OR “neural networks” OR “deep learning” AND “...
Convex formulation of multiple instance learning from positive and unlabeled bags 2018, Neural Networks Citation Excerpt : Note that our proposed method is independent of kernel choices. Other set kernels, such as the conformal kernels (Blaschko & Hofmann, 2006) and affinity propagation clustering-base...
We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Result...
Multiple kernel support vector machine PL-SVM: Support vector machine with polynomial kernel RBF-SVM: Support vector machine with radial basis function kernel SIG-SVM: Support vector machine with sigmoid kernel SimpleMKL: Simple multiple kernel learning SS-SVM: Semi-supervised support vector mac...
Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection PR (2018) X. Guo et al. Robust low-rank subspace segmentation with finite mixture noise PR (2019) S. Hechmi et al. Multi-kernel sparse subspace clustering on the Riemannian manifold of symmetric positive defi...
learning within the field of computer vision. Subsequent innovations include GoogLeNet, proposed by [13], which significantly enhanced network performance without increasing computational demands through the introduction of the Inception module. Later, [14] introduced ResNet’s residual learning framework, ...
Jebara T (2004) Multi-task feature and kernel selection for SVMs. In: Proceedings of the twenty-first international conference on machine learning. ACM, p 55 Ji S, Ye J (2009) An accelerated gradient method for trace norm minimization. In: Proceedings of the 26th annual international conferen...
In advanced machine learning the fuzzy methods are mostly utilized in medical imaging. The existing topic modeling method is based on linear and statistical distribution. This paper presented a new multiple kernel fuzzy topic modeling (MKFTM) approach for biomedical text documents. We also proposed ...
providing explanations about the molecular factors and phenotypes that are driving the classification is crucial to build trust in the performance of the predictive system. We propose Pathway Induced Multiple Kernel Learning (PIMKL), a novel methodology to reliably classify samples that can also help ...