Multi-label punitive kNN with self-adjusting memory for drifting data streams
In multi-label learning, data may simultaneously belong to more than one class. When multi-
label data arrives as a stream, the challenges associated with multi-label learning are joined …
label data arrives as a stream, the challenges associated with multi-label learning are joined …
Amanda: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency
Abstract Concept drift refers to an alteration in the relations between input and output data in
the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual …
the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual …
Sparse filtering based domain adaptation for mechanical fault diagnosis
Z Zhang, H Chen, S Li, Z An - Neurocomputing, 2020 - Elsevier
Recently, machine learning has achieved considerable success in the field of mechanical
fault diagnosis. Nevertheless, in many real-world applications, the original vibration data …
fault diagnosis. Nevertheless, in many real-world applications, the original vibration data …
Sena: Similarity-based error-checking of neural activations
In this work, we propose SENA, a run-time monitor focused on detecting unreliable
predictions from machine learning (ML) classifiers. The main idea is that instead of trying to …
predictions from machine learning (ML) classifiers. The main idea is that instead of trying to …
MPC with machine learning applied to resource allocation problem using lambda architecture
MP Dal Pont, RS Ferreira, WW Teixeira, DM Lima… - IFAC-PapersOnLine, 2019 - Elsevier
The resource allocation problem is the process of allocating limited resources for a vast
amount of tasks. Within this problem there are several important variants such as the …
amount of tasks. Within this problem there are several important variants such as the …
Adaptive Multi-label Classification on Drifting Data Streams
M Roseberry - 2024 - scholarscompass.vcu.edu
Drifting data streams and multi-label data are both challenging problems. When multi-label
data arrives as a stream, the challenges of both problems must be addressed along with …
data arrives as a stream, the challenges of both problems must be addressed along with …
Runtime safety monitoring of ML-based perception functions in autonomous systems
RS Ferreira - 2023 - theses.hal.science
High-accurate machine learning (ML) image classifiers cannot guarantee that they will not
fail at operation. Thus, their deployment in safety-critical applications such as autonomous …
fail at operation. Thus, their deployment in safety-critical applications such as autonomous …
Contrôle de sécurité en cours d'exécution des fonctions de perception basées sur le ML dans les systèmes autonomes
RS Ferreira - 2023 - laas.hal.science
Les classificateurs d'images à apprentissage machine (ML) très précis ne peuvent pas
garantir qu'ils ne tomberont pas en panne en cours de fonctionnement. Par conséquent, leur …
garantir qu'ils ne tomberont pas en panne en cours de fonctionnement. Par conséquent, leur …
[PDF][PDF] Real-time decision support system applied to distribution utility dispatches
M Dal Pont, W Teixeira - 2019 - cired-repository.org
A distribution utility has to deal with several customer calls regarding grid maintenance or
energy issues. Generally, when the proper channels receive a call from the customers, the …
energy issues. Generally, when the proper channels receive a call from the customers, the …
[PDF][PDF] Handshake: classificador para streams de dados em ambientes não estacionários com latência extrema de verificação
LGM Alvim - 2018 - researchgate.net
Com a evolução dos sistemas computacionais e da tecnologia, os computadores
começaram a fazer alguns processos antes realizados por humanos. Classificação é um …
começaram a fazer alguns processos antes realizados por humanos. Classificação é um …