Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness [PDF]
, 2013 We investigate the learning rate of multiple kernel learning (MKL) with $\ell_1$ and elastic-net regularizations. The elastic-net regularization is a composition of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for ...
Sugiyama, Masashi, Suzuki, Taiji
core +2 more sources
Robust Multi-View Subspace Clustering Via Weighted Multi-Kernel Learning and Co-Regularization
IEEE Access, 2020 Using multi-kernel learning to deal with the non-linear relationship of data has become a new research topic in the field of multi-view subspace clustering.
Yilu Zheng +5 more
doaj +1 more source
Insights from classifying visual concepts with multiple kernel learning. [PDF]
PLoS ONE, 2012 Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL)
Alexander Binder +7 more
doaj +1 more source
Adaptive diffusion kernel learning from biological networks for protein function prediction
BMC Bioinformatics, 2008 Background Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction.
Ji Shuiwang, Sun Liang, Ye Jieping
doaj +1 more source
Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation
Remote Sensing, 2018 High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to ...
Tianming Zhan +5 more
doaj +1 more source
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
, 2018 Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than ...
Kang, Zhao +3 more
core +1 more source
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 +8 more
wiley +1 more source
Generalization Guarantees for a Binary Classification Framework for Two-Stage Multiple Kernel Learning [PDF]
, 2013 We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning.
Kar, Purushottam
core +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Efficient Output Kernel Learning for Multiple Tasks [PDF]
, 2015 The paradigm of multi-task learning is that one can achieve better generalization by learning tasks jointly and thus exploiting the similarity between the tasks rather than learning them independently of each other.
Hein, Matthias +3 more
core +1 more source