CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
Learning Deep Belief Networks from Non-Stationary Streams
Authors
R Calandra
MP Deisenroth
FM Pouzols
T Raiko
Publication date
1 January 2012
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. © 2012 Springer-Verlag
Similar works
Full text
Available Versions
Supporting member
Spiral - Imperial College Digital Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:spiral.imperial.ac.uk:1004...
Last time updated on 21/10/2013
TUbiblio
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:tubiblio.ulb.tu-darmstadt....
Last time updated on 05/04/2020
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1007%2F978-3-642-3...
Last time updated on 01/04/2019