Results 41 to 50 of about 29,752 (189)
, 2020 Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction.
Ali, Akhtar +11 more
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Cyclic Nonlinear Correlation Analysis for Time Series
IEEE Access, 2022 Principal component analysis (PCA) and kernel PCA allow the decorrelation of data with respect to a basis that is found via variance maximization. However, these techniques are based on pointwise correlations.
Christopher M. A. Bonenberger +3 more
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Learning with Cross-Kernels and Ideal PCA
CoRR, 2014 We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning. The main potential of cross-kernels lies in the fact that (a) only one side of the matrix scales with the number of data points, and (b) cross-kernels, as opposed to the usual kernel matrices ...
Franz J. Király +2 more
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Association test based on SNP set: logistic kernel machine based test vs. principal component analysis. [PDF]
PLoS ONE, 2012 GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary.
Yang Zhao +5 more
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Evaluation of the Kernel Test Weight and Selection of Identification Indexes of Maize Inbred Lines
AgronomyKernel test weight (KTW) is one of the important assessment indexes of maize quality grade and one of the important influencing factors of yield. This study analyzed 12 traits related to KTW in 321 maize inbred lines using multivariate methods.
Tao Shen +11 more
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Hybrid Artificial Intelligent System For Human Gender Classification [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 This paper introduces an automatic human gender classification (male or female ) depending on ultrasound images using artificial neural network to classify the gender .
Laheeb M. Al-Zoubaidy, Susan H. Mohammad
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Jurnal Ilmu Dasar, 2010 Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data.
Ismail Djakaria +2 more
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Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets
Complexity, 2019 As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling ...
Feng Zhao +5 more
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An Approximate Version of Kernel PCA
2006 5th International Conference on Machine Learning and Applications (ICMLA'06), 2006 We propose an analog of kernel Principal Component Analysis (kernel PCA). Our algorithm is based on an approximation of PCA which uses Gram-Schmidt orthonormalization. We combine this approximation with Support Vector Machine kernels to obtain a nonlinear generalization of PCA.
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International Student Research ReviewThis study presents an improved face recognition system tackling the Eigenface algorithm's limitations regarding lighting variance, class separability, and classification.
Amyr Edmar Francisco +3 more
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