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pro vyhledávání: '"Martí Espelt, Aniol"'
This letter explores a plug-in estimator of second-order Tsallis entropy based on Kernel Density Estimation (KDE) and its implicit regularization process. First, it is shown that the expected value of the estimator corresponds to the entropy of an Ad
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3484::2518e7695880d0da783afadcd1dc2e39
https://hdl.handle.net/2117/390767
https://hdl.handle.net/2117/390767
Autor:
Martí Espelt, Aniol
Gaussianity tests have been used for decades in statistics to determine if a dataset is well modeled by a normal distribution. More recently, they have also found their place in machine learning. The reason of this proliferation is that some parametr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3484::737c3e5d7bae4c9bbe5d01751478c266
https://hdl.handle.net/2117/379854
https://hdl.handle.net/2117/379854
Autor:
Martí Espelt, Aniol
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Universitat Politècnica de Catalunya (UPC)
Recent technological advances have resulted in massive data collection, also in hazard environments such as the sea or space. In these situations, both computing power and downlink bandwidth are quite limited. For this reason, efficient data compress
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2873564e98ee1ece12e6262169add994
https://hdl.handle.net/2117/351141
https://hdl.handle.net/2117/351141