22,001 research outputs found
Selective Sampling with Drift
Recently there has been much work on selective sampling, an online active
learning setting, in which algorithms work in rounds. On each round an
algorithm receives an input and makes a prediction. Then, it can decide whether
to query a label, and if so to update its model, otherwise the input is
discarded. Most of this work is focused on the stationary case, where it is
assumed that there is a fixed target model, and the performance of the
algorithm is compared to a fixed model. However, in many real-world
applications, such as spam prediction, the best target function may drift over
time, or have shifts from time to time. We develop a novel selective sampling
algorithm for the drifting setting, analyze it under no assumptions on the
mechanism generating the sequence of instances, and derive new mistake bounds
that depend on the amount of drift in the problem. Simulations on synthetic and
real-world datasets demonstrate the superiority of our algorithms as a
selective sampling algorithm in the drifting setting
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition
for the implementation of cognitive radio (CR). Collaborative spectrum sensing
among the cognitive radio nodes is expected to improve the ability of checking
complete spectrum usage. Due to hardware limitations, each cognitive radio node
can only sense a relatively narrow band of radio spectrum. Consequently, the
available channel sensing information is far from being sufficient for
precisely recognizing the wide range of unoccupied channels. Aiming at breaking
this bottleneck, we propose to apply matrix completion and joint sparsity
recovery to reduce sensing and transmitting requirements and improve sensing
results. Specifically, equipped with a frequency selective filter, each
cognitive radio node senses linear combinations of multiple channel information
and reports them to the fusion center, where occupied channels are then decoded
from the reports by using novel matrix completion and joint sparsity recovery
algorithms. As a result, the number of reports sent from the CRs to the fusion
center is significantly reduced. We propose two decoding approaches, one based
on matrix completion and the other based on joint sparsity recovery, both of
which allow exact recovery from incomplete reports. The numerical results
validate the effectiveness and robustness of our approaches. In particular, in
small-scale networks, the matrix completion approach achieves exact channel
detection with a number of samples no more than 50% of the number of channels
in the network, while joint sparsity recovery achieves similar performance in
large-scale networks.Comment: 12 pages, 11 figure
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works
without keeping a version space. This is unlike all previous approaches where a
restricted set of candidate hypotheses is maintained throughout learning, and
only hypotheses from this set are ever returned. By avoiding this version space
approach, our algorithm sheds the computational burden and brittleness
associated with maintaining version spaces, yet still allows for substantial
improvements over supervised learning for classification
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
We consider the problem of allocating radio resources over wireless
communication links to control a series of independent wireless control
systems. Low-latency transmissions are necessary in enabling time-sensitive
control systems to operate over wireless links with high reliability. Achieving
fast data rates over wireless links thus comes at the cost of reliability in
the form of high packet error rates compared to wired links due to channel
noise and interference. However, the effect of the communication link errors on
the control system performance depends dynamically on the control system state.
We propose a novel control-communication co-design approach to the low-latency
resource allocation problem. We incorporate control and channel state
information to make scheduling decisions over time on frequency, bandwidth and
data rates across the next-generation Wi-Fi based wireless communication links
that close the control loops. Control systems that are closer to instability or
further from a desired range in a given control cycle are given higher packet
delivery rate targets to meet. Rather than a simple priority ranking, we derive
precise packet error rate targets for each system needed to satisfy stability
targets and make scheduling decisions to meet such targets while reducing total
transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS)
method is tested in numerous simulation experiments that demonstrate its
effectiveness in meeting control-based goals under tight latency constraints
relative to control-agnostic scheduling
- …