Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Leonardo Agustín Hernández-Pérez"'
Publikováno v:
Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030896904
IWAIPR
IWAIPR
This paper addresses two strategies for pattern recognition in high-dimension data sets, obtained from databases of digitally traced neurons. The first strategy has as distinctive characteristic that the features are obtained both from the whole neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eada74893255708cb0f8b5c38c89a06c
https://doi.org/10.1007/978-3-030-89691-1_15
https://doi.org/10.1007/978-3-030-89691-1_15
Autor:
Leonardo Agustín Hernández-Pérez, Juan V. Lorenzo-Ginori, José Daniel López-Cabrera, Rubén Orozco-Morales
Publikováno v:
Journal of neuroscience methods. 343
Background This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software. Methods New morphological features were developed, bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a9d4c759400f249632a8a1044c57422
https://pubmed.ncbi.nlm.nih.gov/32615140
https://pubmed.ncbi.nlm.nih.gov/32615140
Autor:
Rainer Martín-Pérez, Duniel Delgado-Castillo, Juan V. Lorenzo-Ginori, Leonardo Agustín Hernández-Pérez, Rubén Orozco-Morales
Publikováno v:
Neuroinformatics. 17:5-25
This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebb1e2cb4277496a0e7c8b85b62fba8d
https://doi.org/10.1007/s12021-018-9374-0
https://doi.org/10.1007/s12021-018-9374-0
Autor:
Rubén Orozco-Morales, Leonardo Agustín Hernández-Pérez, José Daniel López-Cabrera, Juan V. Lorenzo-Ginori
Publikováno v:
Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030011314
IWAIPR
IWAIPR
The nonlinear dynamic analysis of time series is a powerful tool which has extended its application to many branches of scientific research. Topological equivalence is one of the main concepts that sustain theoretically the nonlinear dynamics procedu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0a72f4f9a7f90ece9441314afbb446d
https://doi.org/10.1007/978-3-030-01132-1_2
https://doi.org/10.1007/978-3-030-01132-1_2