Results 1 to 10 of about 7,040 (158)
Stratified Transfer Learning for Cross-domain Activity Recognition
, 2017 In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none ...
Chen, Yiqiang +4 more
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Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off
, 2018 Kernel methods are powerful learning methodologies that provide a simple way to construct nonlinear algorithms from linear ones. Despite their popularity, they suffer from poor scalability in big data scenarios.
Sriperumbudur, Bharath, Sterge, Nicholas
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Evaluation of PPG Biometrics for Authentication in different states
, 2017 Amongst all medical biometric traits, Photoplethysmograph (PPG) is the easiest to acquire. PPG records the blood volume change with just combination of Light Emitting Diode and Photodiode from any part of the body.
Abbas, Sherif N +2 more
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By-passing the Kohn-Sham equations with machine learning
, 2017 Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics.
Brockherde, Felix +5 more
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Signal processing methods for EEG data classification [PDF]
, 2008 Imperial Users ...
Varnavas, Andreas Soteriou +1 more
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Clustering of Gamma-Ray bursts through kernel principal component analysis
, 2017 We consider the problem related to clustering of gamma-ray bursts (from "BATSE" catalogue) through kernel principal component analysis in which our proposed kernel outperforms results of other competent kernels in terms of clustering accuracy and we ...
Chattopadhyay, Asis Kumar +2 more
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Similarity Learning for Provably Accurate Sparse Linear Classification
, 2012 In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions.
Bellet, Aurelien +2 more
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Kernelized design of experiments [PDF]
This paper describes an approach for selecting instances in regression problems in the cases where observations x are readily available, but obtaining labels y is hard.
Rüping, Stefan, Weihs, Claus
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Generalized KPCA by adaptive rules in feature space
International Journal of Computer Mathematics, 2010Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA).
Yanwei Pang, Yuan Yuan
exaly +2 more sources
KPCA for semantic object extraction in images
Pattern Recognition, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuelong Li, Dacheng Tao
exaly +2 more sources