Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

We consider the problem of unsupervised domain adaptation (DA) in regression under the
assumption of linear hypotheses (eg Beer–Lambert's law)–a task recurrently encountered in …

This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …

In practical applications, the problem of training-and test-samples from different distributions
is often encountered, such as instruments or external environmental factors change when …

Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from
different domains, has gained increasing attention over the past decade. With the current …

In modern manufacturing facilities, there are basically two essential phases for assuring high
production quality with low (or even zero) defects and waste in order to save costs for …

Chemometric modeling for spectral data is considered a key technology in
biopharmaceutical processing to realize real-time process control and release testing …

In chemometrics, calibration model adaptation is desired when training-and test-samples
come from different distributions. Domain-invariant feature representation is currently a …

The problem of transferring calibrations from a primary to a secondary instrument, that is,
calibration transfer (CT), has been a matter of considerable research in chemometrics over …

The complexity involved in the process of real-time data-driven monitoring dynamic systems
for predicted maintenance is usually huge. Up to certain extent, any data-driven approach is …