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Generalized Maximum Entropy for Supervised Classification
(2022-04)
The maximum entropy principle advocates to
evaluate events’ probabilities using a distribution that maximizes
entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
A Deep Learning Approach for Generating Soft Range Information from RF Data
(2022-01-24)
Radio frequency (RF)-based techniques are widely
adopted for indoor localization despite the challenges in extracting
sufficient information from measurements. Soft range
information (SRI) offers a promising alternative ...
Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework
(2022-01-14)
The overall global death rate for COVID-19 patients
has escalated to 2.13% after more than a year of worldwide
spread. Despite strong research on the infection pathogenesis, the
molecular mechanisms involved in a fatal ...
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
(2021-12-30)
Localization systems based on ultra-wide band
(UWB) measurements can have unsatisfactory performance in
harsh environments due to the presence of non-line-of-sight
(NLOS) errors. Learning-based methods for error ...
Deep GEM-based network for weakly supervised UWB ranging error mitigation
(2021-12-30)
Ultra-wideband (UWB)-based techniques, while becoming
mainstream approaches for high-accurate positioning,
tend to be challenged by ranging bias in harsh environments.
The emerging learning-based methods for error ...
Location Awareness in Beyond 5G Networks
(2021-11-01)
Location awareness is essential for enabling contextual
services and for improving network management in 5th
generation (5G) and beyond 5G (B5G) networks. This paper
provides an overview of the expanding opportunities ...
Minimax Classification with 0-1 Loss and Performance Guarantees
(2020-12-01)
Supervised classification techniques use training samples to find classification
rules with small expected 0-1 loss. Conventional methods achieve efficient learning
and out-of-sample generalization by minimizing surrogate ...
General supervision via probabilistic transformations
(2020-08-01)
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
Probabilistic Load Forecasting Based on Adaptive Online Learning
(2020)
Load forecasting is crucial for multiple energy management
tasks such as scheduling generation capacity, planning
supply and demand, and minimizing energy trade costs. Such
relevance has increased even more in recent ...
Soft information for localization-of-things
(2019-11-01)
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such ...