Please use this identifier to cite or link to this item:
Title: Can Metalearning Be Applied to Transfer on Heterogeneous Datasets?
Authors: Catarina Félix Oliveira
Carlos Manuel Soares
Alípio Jorge
Issue Date: 2016
Abstract: Machine learning processes consist in collecting data, obtaining a model and applying it to a given task. Given a new task, the standard approach is to restart the learning process and obtain a new model. However, previous learning experience can be exploited to assist the new learning process. The two most studied approaches for this are meta-learning and transfer learning. Metalearning can be used for selecting the predictive model to use on a new dataset. Transfer learning allows the reuse of knowledge from previous tasks. However, when multiple heterogeneous tasks are available as potential sources for transfer, the question is which one to use. One approach to address this problem is metalearning. In this paper we investigate the feasibility of this approach. We propose a method to transfer weights from a source trained neural network to initialize a network that models a potentially very different target dataset. Our experiments with 14 datasets indicate that this method enables faster convergence without significant difference in accuracy provided that the source task is adequately chosen. This means that there is potential for applying metalearning to support transfer between heterogeneous datasets.
metadata.dc.type: conferenceObject
Appears in Collections:CESE - Articles in International Conferences
LIAAD - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00K-C8M.pdf445.47 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.