@conference{umaquinga2021SVM,

title = {Developments on Support Vector Machines for Multiple-Expert Learning},

author = {Ana C. Umaquinga-Criollo and Juan D. Tamayo Quintero, María N. Moreno-García adn Yahya Aalaila and Diego H. Peluffo-Ordóñez },

editor = {Springer},

url = {https://link.springer.com/chapter/10.1007/978-3-030-91608-4_57},

doi = {https://doi.org/10.1007/978-3-030-91608-4_57},

isbn = {978-3-030-91607-7},

year  = {2021},

date = {2021-11-23},

urldate = {2021-11-23},

booktitle = {International Conference on Intelligent Data Engineering and Automated Learning.},

volume = {13113 },

number = {22},

pages = { 587–598},

publisher = {Intelligent Data Engineering and Automated Learning – IDEAL 2021},

organization = {Spinger},

series = {Intelligent Data Engineering and Automated Learning – IDEAL 2021},

abstract = {In supervised learning scenarios, some applications require solve a classification problem wherein labels are not given as a single ground truth. Instead, the criteria of a set of experts is used to provide labels aimed at compensating for the erroneous influence with respect to a single labeler as well as the error bias (excellent or lousy) due to the level of perception and experience of each expert. This paper aims to briefly outline mathematical developments on support vector machines (SVM), and overview SVM-based approaches for multiple expert learning (MEL). Such MEL approaches are posed by modifying the formulation of a least-squares SVM, which enables to obtain a set of reliable, objective labels while penalizing the evaluation quality of each expert. Particularly, this work studies both two-class (binary) MEL classifier (BMLC) and its extension to multiclass through one-against all (OaA-MLC) including penalization of each expert’s influence. Formal mathematical developments are stated, as well as remarkable discussion on key aspects about the least-squares SVM formulation and penalty factors are provided.},

key = {Multiple expert learning, Supervised learning, Support vector machines},

keywords = {Multiple expert learning, Supervised Learning, Support Vector Machines},

pubstate = {published},

tppubtype = {conference}

}

@conference{umaquinga2018b,

title = {El aprendizaje automático: Importancia, avances, técnicas y aplicaciones. Machine learning: Importance, advances, techniques and applications},

author = {Ana Cristina Umaquinga-Criollo and Luis Edilberto Suárez-Zambrano and Omar Ricardo Oña-Rocha},

editor = {Universidad de Nariño},

url = {https://www.researchgate.net/publication/336749930_El_aprendizaje_automatico_Importancia_avances_tecnicas_y_aplicaciones_Machine_learning_Importance_advances_techniques_and_applications},

isbn = {978-958-8958-65-1.},

year  = {2018},

date = {2018-09-15},

booktitle = {El papel de la Ingeniería en tiempos de construcción de paz y buen vivir.},

pages = {176-186},

publisher = {Universidad de Nariño},

abstract = {Resumen 

 

Este trabajo presenta un estudio descriptivo de las técnicas de aprendizaje automático llamado machine learning, enfocándose en las técnicas de aprendizaje supervisado y no supervisado, con la presentación de las técnicas exitosas para determinados casos de estudio, experimentación y puesta en producción. 

 

Esta investigación pretende ser de aporte a la comunidad científica y empresarial, principalmente investigadores que inician y quienes experimentan sus estudios en análisis comparativos en esta área de vital importancia. 

Este trabajo es de tipo investigativo, documental y exploratorio. Se realiza un recorrido por los principales tipos de aprendizaje automático, avances y proyectos enfocados a Inteligencia Artificial. 

 

Abstract 

 

This paper presents a descriptive study of machine learning techniques called machine learning, focusing on supervised and unsupervised learning techniques, with the presentation of successful techniques for specific cases of study, experimentation and production. 

 

This research aims to be of contribution to the scientific and business community, mainly researchers who initiate and who experience their studies in comparative analysis in this area of vital importance. 

 

This work is of investigative, documentary and exploratory type. A tour through the main types of machine learning, advances and projects focused on Artificial Intelligence.},

keywords = {Aplicaciones de Aprendizaje Automático, Aprendizaje Automático, Aprendizaje no-Supervisado, Aprendizaje Supervisado, Artificial Intelligence, Inteligencia Artificial, Machine Learning, Machine Learning Applications, Supervised Learning, Unsupervised Learning},

pubstate = {published},

tppubtype = {conference}

}