A framework for online prediction using kernel adaptive filtering

Nowadays, the task of predicting in schemas online is an essential field of study for machine learning. The Filters Adaptive based on kernel methods have taken an essential role in this type of task; this is primarily due to their condition of universal approximation, their ability to solve nonlinear problems and the modest computing cost they possess. However, although they have significant advantages with similar methods, they present different challenges to be solved such as: (1) the tuning of the kernel bandwidth parameters and the learning rate; (2) the limitation in the model size, product of the number of elements that the filtered dictionary may contain; and, (3) the efficient construction and modeling of multiple filters. The improvement of these conditions will allow an improvement in the representation of time series dynamics, which translates into a decrease in prediction error. This thesis document addresses the previous issues raised from three proposals. The first is through the interactive search for adequate kernel bandwidth and learning rate, which is achieved by minimizing the correntropy within a proposed cost function. The second contribution corresponds to a scheme of sequential construction of filters, which unlike other methods of state of the art, does not restrict the samples to a single dictionary, and that additionally updates the weights of the samples shared in several filters. The third and last one corresponds to the integration of a kernel bandwidth update method with another that sequentially builds a filter bank. These different proposed frameworks were validated in synthetic data sets as in the real world. The results, in general, show an improvement in the convergence rate, the reduction of the mean square error and the size of the dictionary with different filters of state of the art and a neural network for a specific case.

• Autores: León Gómez, Eder Arley
• Tipo de recurso: Work document
• Fecha de publicación: 2019
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: eng
• OAI Identifier: oai:repositorio.unal.edu.co:unal/75985
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/75985
• Palabra clave: 620 - Ingeniería y operaciones afines; Machine learning; Forecasts; Kernel adaptative filtering; Dictionary; Learning rate; Kernel bandwidth; Clustering adaptive; Aprendizaje de máquina; Predicción; Filtros adaptativos Kernel; Diccionario; Tasa de aprendizaje; Ancho de banda del Kernel; Agrupamiento adaptativo
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional