Sharing to learn and learning to share--Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review
Integrating knowledge across different domains is an essential feature of human learning.
Learning paradigms such as transfer learning, meta learning, and multi-task learning reflect …
Learning paradigms such as transfer learning, meta learning, and multi-task learning reflect …
A novel transfer learning framework for time series forecasting
R Ye, Q Dai - Knowledge-Based Systems, 2018 - Elsevier
Recently, many excellent algorithms for time series prediction issues have been proposed,
most of which are developed based on the assumption that sufficient training data and …
most of which are developed based on the assumption that sufficient training data and …
A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks
Sentiment analysis is crucial in various systems such as opinion mining and predicting.
Considerable research has been done to analyze sentiment using various machine learning …
Considerable research has been done to analyze sentiment using various machine learning …
Hierarchical lifelong learning by sharing representations and integrating hypothesis
In lifelong machine learning (LML) systems, consecutive new tasks from changing
circumstances are learned and added to the system. However, sufficiently labeled data are …
circumstances are learned and added to the system. However, sufficiently labeled data are …
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning
Online federated learning (OFL) and online transfer learning (OTL) are two collaborative
paradigms for overcoming modern machine learning challenges such as data silos …
paradigms for overcoming modern machine learning challenges such as data silos …
A principled approach for learning task similarity in multitask learning
Multitask learning aims at solving a set of related tasks simultaneously, by exploiting the
shared knowledge for improving the performance on individual tasks. Hence, an important …
shared knowledge for improving the performance on individual tasks. Hence, an important …
Task similarity estimation through adversarial multitask neural network
Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting
shared knowledge to improve performance on individual tasks. Though numerous empirical …
shared knowledge to improve performance on individual tasks. Though numerous empirical …
Bayesian transfer learning
Transfer learning is a burgeoning concept in statistical machine learning that seeks to
improve inference and/or predictive accuracy on a domain of interest by leveraging data …
improve inference and/or predictive accuracy on a domain of interest by leveraging data …
Online heterogeneous transfer by hedge ensemble of offline and online decisions
In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the
target data of interest arrive in an online manner, while the source data and auxiliary co …
target data of interest arrive in an online manner, while the source data and auxiliary co …
Boosting based multiple kernel learning and transfer regression for electricity load forecasting
Accurate electricity load forecasting is of crucial importance for power system operation and
smart grid energy management. Different factors, such as weather conditions, lagged …
smart grid energy management. Different factors, such as weather conditions, lagged …