Results 221 to 230 of about 355,362 (266)

An interpretable IGWO-MKRVM model for predicting excavation damaged zone thickness of drift. [PDF]

Sci Rep
Yang R, Zhang G, Ye Y, Wan M.
europepmc   +1 more source

SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification. [PDF]

PLoS One
Jena M, Dehuri S, Cho SB.
europepmc   +1 more source

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Domain Transfer Multiple Kernel Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning ...
Lixin Duan, Ivor W Tsang, Dong Xu
exaly   +4 more sources

Localized algorithms for multiple kernel learning

Pattern Recognition, 2013
Instead of selecting a single kernel, multiple kernel learning (MKL) uses a weighted sum of kernels where the weight of each kernel is optimized during training. Such methods assign the same weight to a kernel over the whole input space, and we discuss localized multiple kernel learning (LMKL) that is composed of a kernel-based learning algorithm and a
Mehmet Gonen
exaly   +3 more sources

Multiple Graph-Kernel Learning

2015 IEEE Symposium Series on Computational Intelligence, 2015
Kernels for structures, including graphs, generally suffer of the diagonally dominant gram matrix issue, the effect by which the number of sub-structures, or features, shared between instances are very few with respect to those shared by an instance with itself.
AIOLLI, FABIO, DONINI, MICHELE, NAVARIN, NICOLO', SPERDUTI, ALESSANDRO   +3 more
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Absent Multiple Kernel Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Multiple kernel learning (MKL) optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels are missing, which is common in practical applications.
Xinwang Liu 0002, Lei Wang 0001, Jianping Yin, Yong Dou, Jian Zhang 0002   +4 more
openaire   +1 more source

Multiple kernel learning with hybrid kernel alignment maximization

Pattern Recognition, 2017
Abstract Two-stage multiple kernel learning (MKL) algorithms have been extensively researched in recent years due to their high efficiency and effectiveness. Previous works have attempted to optimize the combination coefficients by maximizing the centralized kernel alignment between the combined kernel and the ideal kernel.
Yueqing Wang, Xinwang Liu, Yong Dou
exaly   +2 more sources

Multiple Kernel Learning with High Order Kernels

2010 20th International Conference on Pattern Recognition, 2010
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed.
Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Tian 0001   +3 more
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Multiple kernel learning by empirical target kernel

International Journal of Wavelets, Multiresolution and Information Processing, 2019
Multiple kernel learning (MKL) aims at learning an optimal combination of base kernels with which an appropriate hypothesis is determined on the training data. MKL has its flexibility featured by automated kernel learning, and also reflects the fact that typical learning problems often involve multiple and heterogeneous data sources.
Peiyan Wang, Dongfeng Cai
openaire   +2 more sources