[PDF][PDF] Transfer learning for reinforcement learning domains: A survey.
The reinforcement learning paradigm is a popular way to address problems that have only
limited environmental feedback, rather than correctly labeled examples, as is common in …
limited environmental feedback, rather than correctly labeled examples, as is common in …
[LIVRE][B] Metalearning: Applications to data mining
Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …
efficient models and solutions by adapting machine learning and data mining processes …
Co-clustering based classification for out-of-domain documents
In many real world applications, labeled data are in short supply. It often happens that
obtaining labeled data in a new domain is expensive and time consuming, while there may …
obtaining labeled data in a new domain is expensive and time consuming, while there may …
Transfer learning using Kolmogorov complexity: Basic theory and empirical evaluations
M Mahmud, S Ray - Advances in neural information …, 2007 - proceedings.neurips.cc
In transfer learning we aim to solve new problems using fewer examples using information
gained from solving related problems. Transfer learning has been successful in practice …
gained from solving related problems. Transfer learning has been successful in practice …
Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning
S Tang, M Yang, J Bai - PLoS One, 2020 - journals.plos.org
A new computer-aided detection scheme is proposed, the 3D U-Net convolutional neural
network, based on multiscale features of transfer learning to automatically detect pulmonary …
network, based on multiscale features of transfer learning to automatically detect pulmonary …
Boosting for regression transfer via importance sampling
Current instance transfer learning (ITL) methodologies use domain adaptation and sub-
space transformation to achieve successful transfer learning. However, these …
space transformation to achieve successful transfer learning. However, these …
Reuse of neural modules for general video game playing
A general approach to knowledge transfer is introduced in which an agent controlled by a
neural network adapts how it reuses existing networks as it learns in a new domain …
neural network adapts how it reuses existing networks as it learns in a new domain …
On universal transfer learning
MMH Mahmud - Theoretical Computer Science, 2009 - Elsevier
In transfer learning the aim is to solve new learning tasks using fewer examples by using
information gained from solving related tasks. Existing transfer learning methods have been …
information gained from solving related tasks. Existing transfer learning methods have been …
[LIVRE][B] Transfer in reinforcement learning domains
ME Taylor, J Kacprzyk - 2009 - Springer
In reinforcement learning [Sutton and Barto (1998)](RL) problems, learning agents execute
sequential actions with the goal of maximizing a reward signal, which may be time-delayed …
sequential actions with the goal of maximizing a reward signal, which may be time-delayed …
[CITATION][C] Survey on transfer learning research
庄福振， 罗**， 何清， 史忠植 - Journal of Software, 2014