Results 121 to 130 of about 708 (167)

Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative Study. [PDF]

JMIR Med Inform
Li D, Shukla A, Chandaka S, Taylor B, Xu J, Liu M.   +5 more
europepmc   +1 more source

DiagPat: An Explainable Language Detection Model Using EEG Signals. [PDF]

Sensors (Basel)
Keles T, Yildirim K, Tanko D, Tas S, Tasci I, Tasci B, Tasci G, Tuncer T, Dogan S.   +8 more
europepmc   +1 more source

Mobile Tunnel Lining Measurable Image Scanning Assisted by Collimated Lasers. [PDF]

Sensors (Basel)
Wu X, Ma J, Wang J, Song H, Xu J.
europepmc   +1 more source

Decoding imagined speech with delay differential analysis. [PDF]

Front Hum Neurosci
Carvalho VR, Mendes EMAM, Fallah A, Sejnowski TJ, Comstock L, Lainscsek C.   +5 more
europepmc   +1 more source

A Review of Pedestrian Trajectory Prediction Methods Based on Deep Learning Technology. [PDF]

Sensors (Basel)
Gu X, Li C, Gao L, Niu X.
europepmc   +1 more source

Multikernel Correntropy for Robust Learning [PDF]

IEEE Transactions on Cybernetics, 2022
As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel.
Zejian Yuan, Yuqing Xie, Jing Qin
exaly   +3 more sources

Maximum correntropy unscented filter [PDF]

International Journal of Systems Science, 2017
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function.
Paul Honeine, Xi Liu, Badong Chen
exaly   +3 more sources

Maximum correntropy Kalman filter [PDF]

Automatica, 2017
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously.
JOSÉ Principe, Xi Liu, Haiquan Zhao
exaly   +4 more sources

The correntropy MACE filter

Pattern Recognition, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
JOSÉ Principe, Seungju Han, Erion Hasanbelliu   +2 more
exaly   +4 more sources

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Kernel recursive maximum correntropy

Signal Processing, 2015
In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum correntropy (KRMC), is derived in kernel space and under the maximum correntropy criterion (MCC). The proposed algorithm is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive ...
Wentao Ma, Badong Chen
exaly   +2 more sources