Výsledky vyhledávání - Quantized kernel recursive minimum error entropy

Quantized kernel recursive minimum error entropy algorithm Autor Jiang, Wang, Gao, Yuyi, He, Yue, Chen, Shanmou

“…In this paper, we propose a online vector quantization (VQ) method based on the kernel recursive minimum error entropy (KRMEE) algorithm…”
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Generalized minimum error entropy for robust learning Autor He, Jiacheng, Wang, Gang, Cao, Kui, Diao, He, Wang, Guotai, Peng, Bei

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Diffusion Quantized Recursive Mixture Minimum Error Entropy Algorithm Autor Cai, Peng, Wang, Shiyuan

“…The minimum error entropy (MEE) criterion is widely used in distributed estimation, since it is insensitive to many types of non-Gaussian noises…”
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Mixture quantized error entropy for recursive least squares adaptive filtering Autor He, Jiacheng, Wang, Gang, Peng, Bei, Sun, Qi, Feng, Zhenyu, Zhang, Kun

“… To further improve learning performance, two concepts using a mixture of two Gaussian functions as kernel functions, called mixture error entropy and mixture quantized error entropy, are proposed in this paper…”
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Kernel Risk-Sensitive Mean p-Power Error Algorithms for Robust Learning Autor Zhang, Tao, Wang, Shiyuan, Zhang, Haonan, Xiong, Kui, Wang, Lin

“… To address this issue, a convex kernel risk-sensitive loss (KRL) is proposed to measure the similarity in RKHS, which is the risk-sensitive loss defined as the expectation of an exponential function of the squared estimation error…”
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Quantized criterion-based kernel recursive least squares adaptive filtering for time series prediction Autor He, Jiacheng, Wang, Gang, Zhang, Kun, Zhong, Shan, Peng, Bei

“…The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE…”
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Generalized Minimum Error with Fiducial Points Criterion for Robust Learning Autor Zhao, Haiquan, Gao, Yuan, Zhu, Yingying

“…The conventional Minimum Error Entropy criterion (MEE) has its limitations, showing reduced sensitivity to error mean values and uncertainty regarding error probability density function locations…”
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