Zobrazeno 1 - 10
of 994
pro vyhledávání: '"Jose C. Principe"'
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 2, p 157 (2021)
Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especial
Externí odkaz:
https://doaj.org/article/e2e093ce75304cffb201036ea0d2c50f
Publikováno v:
Entropy, Vol 17, Iss 8, Pp 5549-5560 (2015)
The minimum error entropy (MEE) criterion is an important learning criterion in information theoretical learning (ITL). However, the MEE solution cannot be obtained in closed form even for a simple linear regression problem, and one has to search it,
Externí odkaz:
https://doaj.org/article/f585ad8de20f4787a94f2bc26235091b
Publikováno v:
Entropy, Vol 17, Iss 9, Pp 5995-6006 (2015)
Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS) algorithm, as a popular tool, achieves excellent performance for sparse system identification.
Externí odkaz:
https://doaj.org/article/0606ff24e11d4ab581ba3084600fb8b5
Publikováno v:
Entropy, Vol 17, Iss 5, Pp 3419-3437 (2015)
Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. In particular, in sparse channel estimation, the parameter vector with sparsity chara
Externí odkaz:
https://doaj.org/article/c7354efd44854033b44adf33061b6c5d
Publikováno v:
Entropy, Vol 16, Iss 4, Pp 2223-2233 (2014)
The minimum error entropy (MEE) criterion has been successfully used in fields such as parameter estimation, system identification and the supervised machine learning. There is in general no explicit expression for the optimal MEE estimate unless som
Externí odkaz:
https://doaj.org/article/80c3024a337d474cb5233c6f3fd93eb2
Publikováno v:
Entropy, Vol 16, Iss 2, Pp 814-824 (2014)
The minimum error entropy (MEE) estimation is concerned with the estimation of a certain random variable (unknown variable) based on another random variable (observation), so that the entropy of the estimation error is minimized. This estimation meth
Externí odkaz:
https://doaj.org/article/0b90ef5a5d5c40c5b867639abab79ca3
Publikováno v:
Entropy, Vol 15, Iss 5, Pp 1609-1623 (2013)
The use of a metric to assess distance between probability densities is an important practical problem. In this work, a particular metric induced by an α-divergence is studied. The Hellinger metric can be interpreted as a particular case within the
Externí odkaz:
https://doaj.org/article/9020674299f44c2ea68f24a1f9d96d04
Autor:
Badong Chen, Jose C. Principe
Publikováno v:
Entropy, Vol 14, Iss 11, Pp 2311-2323 (2012)
Recent studies suggest that the minimum error entropy (MEE) criterion can outperform the traditional mean square error criterion in supervised machine learning, especially in nonlinear and non-Gaussian situations. In practice, however, one has to est
Externí odkaz:
https://doaj.org/article/e29ee42e112b4f128d4304ff4b3ce1aa
Autor:
Badong Chen, Jose C. Principe
Publikováno v:
Entropy, Vol 14, Iss 5, Pp 966-977 (2012)
The minimum error entropy (MEE) criterion has been receiving increasing attention due to its promising perspectives for applications in signal processing and machine learning. In the context of Bayesian estimation, the MEE criterion is concerned with
Externí odkaz:
https://doaj.org/article/c18044a3a5dc4afd87deb717b03b3d08
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2004, Iss 13, Pp 2034-2041 (2004)
Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the follow
Externí odkaz:
https://doaj.org/article/0b3688229ba641f3a10f02a78acae48a