11,536 research outputs found
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELC
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Approximate Decentralized Bayesian Inference
This paper presents an approximate method for performing Bayesian inference
in models with conditional independence over a decentralized network of
learning agents. The method first employs variational inference on each
individual learning agent to generate a local approximate posterior, the agents
transmit their local posteriors to other agents in the network, and finally
each agent combines its set of received local posteriors. The key insight in
this work is that, for many Bayesian models, approximate inference schemes
destroy symmetry and dependencies in the model that are crucial to the correct
application of Bayes' rule when combining the local posteriors. The proposed
method addresses this issue by including an additional optimization step in the
combination procedure that accounts for these broken dependencies. Experiments
on synthetic and real data demonstrate that the decentralized method provides
advantages in computational performance and predictive test likelihood over
previous batch and distributed methods.Comment: This paper was presented at UAI 2014. Please use the following BibTeX
citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and
Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference},
Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}
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