40,172 research outputs found
Machine learning and deep learning
Today, intelligent systems that offer artificial intelligence capabilities
often rely on machine learning. Machine learning describes the capacity of
systems to learn from problem-specific training data to automate the process of
analytical model building and solve associated tasks. Deep learning is a
machine learning concept based on artificial neural networks. For many
applications, deep learning models outperform shallow machine learning models
and traditional data analysis approaches. In this article, we summarize the
fundamentals of machine learning and deep learning to generate a broader
understanding of the methodical underpinning of current intelligent systems. In
particular, we provide a conceptual distinction between relevant terms and
concepts, explain the process of automated analytical model building through
machine learning and deep learning, and discuss the challenges that arise when
implementing such intelligent systems in the field of electronic markets and
networked business. These naturally go beyond technological aspects and
highlight issues in human-machine interaction and artificial intelligence
servitization.Comment: Published online first in Electronic Market
Classification in Networked Data: A Toolkit and a Univariate Case Study
This paper1 is about classifying entities that are interlinked with entities for which the class is
known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked
data, and a case-study of its application to networked data used in prior machine learning
research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier,
a relational classifier, and a collective inference procedure. Various existing node-centric
relational learning algorithms can be instantiated with appropriate choices for these components,
and new combinations of components realize new algorithms. The case study focuses on univariate
network classification, for which the only information used is the structure of class linkage in
the network (i.e., only links and some class labels). To our knowledge, no work previously has
evaluated systematically the power of class-linkage alone for classification in machine learning
benchmark data sets. The results demonstrate that very simple network-classification models perform
quite well—well enough that they should be used regularly as baseline classifiers for studies
of learning with networked data. The simplest method (which performs remarkably well) highlights
the close correspondence between several existing methods introduced for different purposes—that
is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study
also shows that there are two sets of techniques that are preferable in different situations, namely
when few versus many labels are known initially. We also demonstrate that link selection plays an
important role similar to traditional feature selectionNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
ODE: A Data Sampling Method for Practical Federated Learning with Streaming Data and Limited Buffer
Machine learning models have been deployed in mobile networks to deal with
the data from different layers to enable automated network management and
intelligence on devices. To overcome high communication cost and severe privacy
concerns of centralized machine learning, Federated Learning (FL) has been
proposed to achieve distributed machine learning among networked devices. While
the computation and communication limitation has been widely studied in FL, the
impact of on-device storage on the performance of FL is still not explored.
Without an efficient and effective data selection policy to filter the abundant
streaming data on devices, classical FL can suffer from much longer model
training time (more than ) and significant inference accuracy
reduction (more than ), observed in our experiments. In this work, we take
the first step to consider the online data selection for FL with limited
on-device storage. We first define a new data valuation metric for data
selection in FL: the projection of local gradient over an on-device data sample
onto the global gradient over the data from all devices. We further design
\textbf{ODE}, a framework of \textbf{O}nline \textbf{D}ata s\textbf{E}lection
for FL, to coordinate networked devices to store valuable data samples
collaboratively, with theoretical guarantees for speeding up model convergence
and enhancing final model accuracy, simultaneously. Experimental results on one
industrial task (mobile network traffic classification) and three public tasks
(synthetic task, image classification, human activity recognition) show the
remarkable advantages of ODE over the state-of-the-art approaches.
Particularly, on the industrial dataset, ODE achieves as high as
speedup of training time and increase in final inference accuracy, and is
robust to various factors in the practical environment
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