Results 51 to 60 of about 355,213 (122)

Large-Margin Determinantal Point Processes [PDF]

, 2014
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset.
Chao, Wei-lun, Gong, Boqing, Grauman, Kristen, Sha, Fei   +3 more
core  

A Multiple Kernel Learning Approach for Air Quality Prediction

Advances in Meteorology, 2018
Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted ...
Hong Zheng, Haibin Li, Xingjian Lu, Tong Ruan   +3 more
doaj   +1 more source

Energy Commodity Price Forecasting with Deep Multiple Kernel Learning

Energies, 2018
Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions ...
Shian-Chang Huang, Cheng-Feng Wu
doaj   +1 more source

Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier

Mathematics
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization.
Tao Yang
doaj   +1 more source

Hybrid intelligent deep kernel incremental extreme learning machine based on differential evolution and multiple population grey wolf optimization methods

Automatika, 2019
Focussing on the problem that redundant nodes in the kernel incremental extreme learning machine (KI-ELM) which leads to ineffective iteration increase and reduce the learning efficiency, a novel improved hybrid intelligent deep kernel incremental ...
Di Wu, Zong Shun Qu, Feng Jiao Guo, Xiao Lin Zhu, Qin Wan   +4 more
doaj   +1 more source

Integrating semantic information into multiple kernels for protein-protein interaction extraction from biomedical literatures.

PLoS ONE, 2014
Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results.
Lishuang Li, Panpan Zhang, Tianfu Zheng, Hongying Zhang, Zhenchao Jiang, Degen Huang   +5 more
doaj   +1 more source

Multiple Kernel Spectral Regression for Dimensionality Reduction

Journal of Applied Mathematics, 2013
Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples.
Bing Liu, Shixiong Xia, Yong Zhou
doaj   +1 more source

Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions

Frontiers in Genetics, 2019
Long non-coding RNAs (lncRNAs) constitute a large class of transcribed RNA molecules. They have a characteristic length of more than 200 nucleotides which do not encode proteins.
Cong Shen, Yijie Ding, Jijun Tang, Jijun Tang, Fei Guo   +4 more
doaj   +1 more source

Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records. [PDF]

Digit Health, 2023
Hayakawa T, Nagashima T, Akimoto H, Minagawa K, Takahashi Y, Asai S.   +5 more
europepmc   +1 more source

Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy. [PDF]

Sensors (Basel), 2022
Tao L, Cao T, Wang Q, Liu D, Sun J.
europepmc   +1 more source