Automatic Domain Partitioning for Multi-Domain Learning
نویسندگان
چکیده
Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata attributes available, it is not always straightforward to select a single best attribute for domain partition, and it is possible that combining more than one metadata attributes (including continuous attributes) can lead to better MDL performance. In this work, we propose an automatic domain partitioning approach that aims at providing better domain identities for MDL. We use a supervised clustering approach that learns the domain distance between data instances , and then cluster the data into better domains for MDL. Our experiment on real multi-domain datasets shows that using our automatically generated domain partition improves over popular MDL methods.
منابع مشابه
Automatic Partitioning for Multi-Agent Reinforcement Learning
This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple agents, without a pri-ori domain knowledge regarding task structures. Partitioning a state/input space into multiple regions helps to exploit the diierential characteristics of regions and diierential characteristics of agents, thus facilitating learning and reducing the complexity of agents especi...
متن کاملPartitioning in reinforcement learning
|This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple agents, without a priori domain knowledge regarding task structures. Partitioning a state/input space into multiple regions helps to exploit diierential characteristics of regions and diierential characteristics of agents, thus facilitating learning and reducing the complexity of agents especially...
متن کاملProviding a structural model for psychological problems based on disconnection and rejection domain and negative automatic thoughts with mediating role of experimental avoidance
Introduction: Psychological problems are the result of a person's interaction with the environment and include behaviors that cause social conflicts, dissatisfaction and individual unhappiness. The present study aimed to provide a structural model for psychological problems based on disconnection and rejection domain and negative automatic thoughts with mediating role of experimental avoidance....
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملMulti-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks
The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image ...
متن کامل