Results 101 to 110 of about 3,257 (211)

EVALUATE DISSIMILARITY OF SAMPLES IN FEATURE SPACE FOR IMPROVING KPCA

Since in the feature space the eigenvector is a linear combination of all the samples from the training sample set, the computational efficiency of KPCA-based feature extraction falls as the training sample set grows.
JINGYU YANG, DAVID ZHANG, JIAN YANG, JIN ZHONG, XU YONG   +4 more
core  

A KPCA-ISSA-SVM Hybrid Model for Identifying Sources of Mine Water Inrush Using Hydrochemical Indicators

Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction ...
Jingzhong Zhu, Qiqing Wang, Baolei Xie, Xikun Lu   +3 more
core   +1 more source

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel
Jianyong Li, Zhiyan Zhang, Xiaoliang Yang, Hailiang Zhao, Aobo Deng, Zhiwen Wang   +5 more
core   +1 more source

KPCA for Thrust Vectoring Systems Exhibiting Singular Points

CoRR
This paper considers a class of thrust vectoring systems, which are nonlinear, overactuated, and time-invariant. We assume that the system is composed of two subsystems and there exist singular points around which the linearized system is uncontrollable. Furthermore, we assume that the system is stabilizable through a two-level control allocation.
Tam W. Nguyen, Kyoungseok Han, Kenji Hirata   +2 more
openaire   +3 more sources

KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA [PDF]

Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image ...
Ali, Mehmet, Prasad, Gaurav, Yang, Wenbo, Tang, Sheyang, Sivakaran, Thanushon, Karmani, Sachin   +5 more
core   +1 more source

Study on Application of Support Vector Machine for Classification Based on KPCA and RS Theory

, 2008
中文摘要:核主成分分析 KPCA是近年来提出的一个十分有效的数据降维方法 ,但它并不能保证所提取的第 一主成分最适用于降维后的数据分类。粗糙集 RS理论是处理这类问题的一个有效方法。提出一个基于 KPCA与 RS理论的支持向量分类机 SVC ,利用 RS理论和信息熵原理对运用 KPCA 进行特征提取后的训练 样本进行特征选择 ,保留重要特征 ,力求减小求解问题的规模 ,提高 SVC的性能。在构建 2006 年上市公司财务困境预警模型的数值实验中 ,以 KPCA、 RS理论作为前置系统的 ...
袁美玲, 王桂明
core  

Cancer-associated mesothelial cells drive immune escape and therapy resistance in ovarian cancer. [PDF]

J Immunother Cancer
Chauvin M, Roche-Prellezo J, Lafont V, Michaud HA, Tromelin E, Michel R, Freixinos C, Meinsohn MC, Colombo PE, Bonnefoy N, Gros L, Pépin D.   +11 more
europepmc   +1 more source

Cave reservoir characterization method driven by GA-KPCA and geological knowledge. [PDF]

PLoS One
Ren W, Chen H, Wang R, Zhang T, Li L.
europepmc   +1 more source

Fault Detection Approach of Cyclotron Ion Sources Based on KPCA-ISSA-SVM. [PDF]

Sensors (Basel)
Li Y, Liu Y, Guan F, Zhang H, Hou S, Huang P, Nong Z.   +6 more
europepmc   +1 more source

Chemotherapy induces an IL1β-dependent neutrophil recruitment and activation that promote chemoresistance in metastatic ovarian cancer. [PDF]

J Immunother Cancer
Miyamoto T, Ye Y, Soliman MM, Manning BS, Murphy B, Zhu L, Shao C, Mirji G, Minakuchi M, Hamada K, Mizuno R, Taki M, Yamanoi K, Murakami R, Mandai M, Ye Y, Wickramasinghe J, Nefedova Y, Shinde R, Kossenkov A, Zhang N.   +20 more
europepmc   +1 more source