Martinez-Villaseñor, Lourdes
Thumbnail Image
Preferred name
Martinez-Villaseñor, Lourdes
Official Name
Martínez Villaseñor, María de Lourdes
Alternative Name
lmartine
Main Affiliation
ORCID
Scopus Author ID
55521085100
Researcher ID
N-7607-2018

Research Output

Filters
2
1 1
Reset filters

Settings
Now showing 1 - 2 of 2
No Thumbnail Available
Publication

A Method to Improve Speed of Training Algorithm in Artificial Hydrocarbon Networks

2019 , Campos Souza, Paulo V. de , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Artificial hydrocarbon networks (AHN) is a supervised machine learning method inspired on chemical carbon networks that simulate heuristic chemical rules involved within organic molecules to represent the structure and behavior of data. However, training AHN depends on a relevant number of parameters. In that sense, the original training algorithm presents some issues to find suitable parameters in a reasonable amount of time. Thus, this paper proposes a new training algorithm for AHN based on the concept of extreme learning machines, to update weight parameters related to the molecular functions. To evaluate the effectiveness of the proposed algorithm, binary classification and regression tests are performed over real public datasets from a central data repository specialized in machine learning problems. The results obtained validated that the updating of the weight parameters using the new training algorithm in the molecular structures is efficient and maintains the expected results of model accuracy. In addition, this work increased up to 24.88% the speed of the training phase in contrast to the original algorithm. © 2019 IEEE.

View
No Thumbnail Available
Publication

Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition

2015 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis

In recent years computing and sensing technologies advances contribute to develop effective human activity recognition systems. In context-aware and ambient assistive living applications, classification of body postures and movements, aids in the development of health systems that improve the quality of life of the disabled and the elderly. In this paper we describe a comparative analysis of data-driven activity recognition techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). We prove that artificial hydrocarbon networks are suitable for efficient body postures and movements classification, providing a comparison between its performance and other well-known supervised learning methods.

View