Ignacio Arroyo-Fernández
Estudiantes de doctorado
iarroyof{1}iingen{2}unam{3}mx - [1:at, 2:dot, 3:dot]+52(55) 56233600 ext. 8807
Currently I'm working on theoretical issues related to adaptive learning of operators on Hilbert spaces. This theory is aimed to leverage Natural Language Understanding applications, i.e. learning reproducing kernels for semantic assessments. These learning procedures are also related to spectral theory, which concepts are useful for designing adaptive and distributed learning systems. In this way we can find difficult machine learning problems, which appear in different analysis levels of Natural Language (phonology, lexicon, syntax, semantics and pragmatics). The particular related issue I deal with at the moment is algorithmic stability induction in NLP.
Líneas de investigación
- Fuzzy systems
- Aprendizaje de máquina
- Kernel Methods
- Procesamiento del lenguiaje natural
- Red neuronal
- Procesamiento de señal
- Modelos de espacio vectorial
Últimas publicaciones
- 2015 Learning Kernels for Semantic Clustering: A Deep Approach
- 2015 Learning Kernels for Distributional Semantic Embeddings
- 2013 Evaluación de dos técnicas de reconocimiento de patrones para su implementación en el Simulador de pilotaje automático de taller del STC Metro de la Cd. de México
- 2013 FPGA-Based architecture for extended associative memories and its application in image recognition
Some inspiring publications:
- Zadeh, Lotfi A. "Fuzzy sets." Information and control 8.3 (1965): 338-353.
- Vapnik, V. N. (1998). Statistical learning theory (Vol. 1). New York: Wiley.
- Lanckriet, Gert RG, et al. "Learning the kernel matrix with semidefinite programming." The Journal of Machine Learning Research 5 (2004): 27-72.
- Băzăvan, Eduard Gabriel, Fuxin Li, and Cristian Sminchisescu. "Fourier kernel learning." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 459-473.
- Cortes, Corinna, Mehryar Mohri, and Afshin Rostamizadeh. "L 2 regularization for learning kernels." Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009.
- Cortes, Corinna, Mehryar Mohri, and Jason Weston. "A general regression technique for learning transductions." Proceedings of the 22nd international conference on Machine learning. ACM, 2005.
- Proakis, J. G., & Manolakis, D. G. (1996). Digital signal processing: principles, algorithms, and applications. Prentice Hall International Editions, 21, 35-41.
- Coughlin, R.F., Driscoll, F.F. (2001). Operational Amplifiers and Linear Integrated Circuits. Prentice Hall.
- Harris, Z.S.: Mathematical Structures of Language. Wiley, New York, NY, USA (1968)