L
Luis Villaseñor-Pineda
Researcher at National Institute of Astrophysics, Optics and Electronics
Publications - 112
Citations - 1449
Luis Villaseñor-Pineda is an academic researcher from National Institute of Astrophysics, Optics and Electronics. The author has contributed to research in topics: Question answering & Imagined speech. The author has an hindex of 19, co-authored 112 publications receiving 1208 citations.
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Book ChapterDOI
Authorship attribution using word sequences
TL;DR: This paper proposes a new method for authorship attribution supported on the idea that a proper identification of authors must consider both stylistic and topic features of texts, which outperforms most current state-of-the-art approaches.
Book ChapterDOI
Dynamic reward shaping: training a robot by voice
TL;DR: A dynamic reward shaping approach, in which extra rewards are not consistently given, can vary with time and may sometimes be contrary to what is needed for achieving a goal.
Journal ArticleDOI
Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification
Alejandro A. Torres-García,Carlos A. Reyes-García,Luis Villaseñor-Pineda,Gregorio Garcia-Aguilar +3 more
TL;DR: The present research is focused on the recognition of five Spanish words corresponding to the English words "up," "down," "left," "right" and "select", with which a computer cursor could be controlled, and shows a dependence relationship between EEG data and imagined words.
Journal ArticleDOI
Discriminative subprofile-specific representations for author profiling in social media
A. Pastor López-Monroy,Manuel Montes-y-Gómez,Hugo Jair Escalante,Luis Villaseñor-Pineda,Efstathios Stamatatos +4 more
TL;DR: A representation for documents that capture discriminative and subprofile-specific information of terms and is in agreement with previous studies on handcrafted attributes for AP is proposed.
Journal ArticleDOI
Transfer learning in imagined speech EEG-based BCIs
Jesús S. García-Salinas,Luis Villaseñor-Pineda,Carlos A. Reyes-García,Alejandro A. Torres-García +3 more
TL;DR: The proposed method extracts characteristic units (i.e. codewords) of the EEGs associated with the words of an initial vocabulary, and then a classification algorithm is applied, which shows no statistical difference.