92,096 research outputs found
MSVMpack: a Multi-Class Support Vector Machine Package
International audienceThis paper describes MSVMpack, an open source software package dedicated to our generic model of multi-class support vector machine. All four multi-class support vector machines (M-SVMs) proposed so far in the literature appear as instances of this model. MSVMpack provides for them the first unified implementation and offers a convenient basis to develop other instances. This is also the first parallel implementation for M-SVMs. The package consists in a set of command-line tools with a callable library. The documentation includes a tutorial, a user's guide and a developer's guide
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
By deploying machine-learning algorithms at the network edge, edge learning
can leverage the enormous real-time data generated by billions of mobile
devices to train AI models, which enable intelligent mobile applications. In
this emerging research area, one key direction is to efficiently utilize radio
resources for wireless data acquisition to minimize the latency of executing a
learning task at an edge server. Along this direction, we consider the specific
problem of retransmission decision in each communication round to ensure both
reliability and quantity of those training data for accelerating model
convergence. To solve the problem, a new retransmission protocol called
data-importance aware automatic-repeat-request (importance ARQ) is proposed.
Unlike the classic ARQ focusing merely on reliability, importance ARQ
selectively retransmits a data sample based on its uncertainty which helps
learning and can be measured using the model under training. Underpinning the
proposed protocol is a derived elegant communication-learning relation between
two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data
uncertainty. This relation facilitates the design of a simple threshold based
policy for importance ARQ. The policy is first derived based on the classic
classifier model of support vector machine (SVM), where the uncertainty of a
data sample is measured by its distance to the decision boundary. The policy is
then extended to the more complex model of convolutional neural networks (CNN)
where data uncertainty is measured by entropy. Extensive experiments have been
conducted for both the SVM and CNN using real datasets with balanced and
imbalanced distributions. Experimental results demonstrate that importance ARQ
effectively copes with channel fading and noise in wireless data acquisition to
achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2)
consideration of imbalanced classification in the experiments. Submitted to
IEEE Journal for possible publicatio
Automatic multi-label subject indexing in a multilingual environment
This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages
Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines
This paper presents a novel framework for designing support vector machines
(SVMs), which does not impose restriction on the SVM kernel to be
positive-definite and allows the user to define memory constraint in terms of
fixed template vectors. This makes the framework scalable and enables its
implementation for low-power, high-density and memory constrained embedded
application. An efficient hardware implementation of the same is also
discussed, which utilizes novel low power memtransistor based cross-bar
architecture, and is robust to device mismatch and randomness. We used
memtransistor measurement data, and showed that the designed SVMs can achieve
classification accuracy comparable to traditional SVMs on both synthetic and
real-world benchmark datasets. This framework would be beneficial for design of
SVM based wake-up systems for internet of things (IoTs) and edge devices where
memtransistors can be used to optimize system's energy-efficiency and perform
in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201
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