105 citations · 156 across the 6 of their papers we have counts for
6 papers
Active learning for data streams: a survey
Davide Cacciarelli, Murat Kulahci
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associ…
Robust online active learning
Davide Cacciarelli, Murat Kulahci, John Sølve Tyssedal
In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipm…
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders
Davide Cacciarelli, Murat Kulahci, John Tyssedal
Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routin…
Stream-based active learning with linear models
Davide Cacciarelli, Murat Kulahci, John Sølve Tyssedal
The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high…
Real-time Monitoring of Functional Data
Fabio Centofanti, Antonio Lepore, Murat Kulahci +1
With the rise of Industry 4.0, huge amounts of data are now generated that are apt to be modelled as functional data. In this setting, standard profile monitoring methods aim to as…
Model based Level Shift Detection in Autocorrelated Data Streams using a moving window
Jacob Søgaard Larsen, Anders Stockmarr, Bjarne Kjær Ersbøll +1
Standard Control Chart techniques to detect level shift in data streams assume independence between observations. As data today is collected with high frequency, this assumption is…