Results 221 to 230 of about 156,082 (279)

Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li7SiPS8 Using Bayesian Optimization

Angewandte Chemie, EarlyView.
Bayesian optimization was used to optimize the synthesis parameters of the solid electrolyte Li7SiPS8$\mathrm {Li}_7\mathrm {SiPS}_8$ in order to increase the ionic conductivity. After only 32 iterations, the ionic conductivity was successfully increased from 2 to 7 mS cm−1$\mathrm {cm}^{-1}$ at 25∘C$^\circ\mathrm {C}$, while synthesis temperature and ...
Lucas G. Balzat, Robert Calaminus, Yinghan Zhao, Kristina Gjorgjevikj, Igor Moudrakovski, Simon Krause, Arnd Koeppe, Britta Nestler, Bettina V. Lotsch   +8 more
wiley   +1 more source

Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning. [PDF]

Brief Bioinform
Cao H, Wang T, Xu Z, Zhao X, Liu G, Yang X, Fang R, Luo Y, Zeng P, Yu H, Zhang Y, Cui Y.   +11 more
europepmc   +1 more source

Multi-Omics Data Integration for Improved Cancer Subtyping via Denoising Autoencoder-Based Multi-Kernel Learning. [PDF]

Genes (Basel)
Yao X, Wang T, Yang Q, Wang J, Qi Y, Xu T, Wei Z, Cui Y, Cao H, Yun K.   +9 more
europepmc   +1 more source

Morphological diversity of Chinese chinquapin (Castanea henryi (Skan) Rehder & E.H. Wilson) germplasm. [PDF]

BMC Plant Biol
Zhu Z, Xu B, Zhao J, Wang L, Gao C, Wang J, Li J.   +6 more
europepmc   +1 more source

Study on the spatial distribution characteristics and influencing factors of older adult(s) care facilities in Liaoning Province based on POI data. [PDF]

Front Public Health
Sun J, Xue X, Cui Z.
europepmc   +1 more source

Multivariate analysis of wild Prunus × keredjensis (Browicz) A.E.Murray based on morphological and pomological characterizations. [PDF]

Sci Rep
Khadivi A, Mirheidari F, Tunç Y.
europepmc   +1 more source

A Bayesian Framework with Dirichlet Priors and Spatial Smoothing for Protein Rotamer Prediction. [PDF]

Int J Mol Sci
Al Nasr K, Jad Allah A, Alamri M, Al Sallal M.   +3 more
europepmc   +1 more source

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Projective Multiple Kernel Subspace Clustering

IEEE Transactions on Multimedia, 2022
Multiple kernel subspace clustering (MKSC), as an important extension for handling multi-view non-linear subspace data, has shown notable success in a wide variety of machine learning tasks. The key objective of MKSC is to build a flexible and appropriate graph for clustering from the kernel space.
Siwei Wang, , Xinwang Liu
exaly   +2 more sources

Multiple kernel clustering based on centered kernel alignment

Pattern Recognition, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianfeng Lu, Chunhua Shen
exaly   +4 more sources