19,021 research outputs found
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This paper presents a generic Bayesian framework that enables any deep
learning model to actively learn from targeted crowds. Our framework inherits
from recent advances in Bayesian deep learning, and extends existing work by
considering the targeted crowdsourcing approach, where multiple annotators with
unknown expertise contribute an uncontrolled amount (often limited) of
annotations. Our framework leverages the low-rank structure in annotations to
learn individual annotator expertise, which then helps to infer the true labels
from noisy and sparse annotations. It provides a unified Bayesian model to
simultaneously infer the true labels and train the deep learning model in order
to reach an optimal learning efficacy. Finally, our framework exploits the
uncertainty of the deep learning model during prediction as well as the
annotators' estimated expertise to minimize the number of required annotations
and annotators for optimally training the deep learning model.
We evaluate the effectiveness of our framework for intent classification in
Alexa (Amazon's personal assistant), using both synthetic and real-world
datasets. Experiments show that our framework can accurately learn annotator
expertise, infer true labels, and effectively reduce the amount of annotations
in model training as compared to state-of-the-art approaches. We further
discuss the potential of our proposed framework in bridging machine learning
and crowdsourcing towards improved human-in-the-loop systems
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
Neuromorphic hardware platforms, such as Intel's Loihi chip, support the
implementation of Spiking Neural Networks (SNNs) as an energy-efficient
alternative to Artificial Neural Networks (ANNs). SNNs are networks of neurons
with internal analogue dynamics that communicate by means of binary time
series. In this work, a probabilistic model is introduced for a generalized
set-up in which the synaptic time series can take values in an arbitrary
alphabet and are characterized by both causal and instantaneous statistical
dependencies. The model, which can be considered as an extension of exponential
family harmoniums to time series, is introduced by means of a hybrid
directed-undirected graphical representation. Furthermore, distributed learning
rules are derived for Maximum Likelihood and Bayesian criteria under the
assumption of fully observed time series in the training set.Comment: Published in IEEE ICASSP 2019. Author's Accepted Manuscrip
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
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