Gaussian mixture model using semisupervised learning for probabilistic fault diagnosis under new data categories
Fault diagnosis has played a vital role in industry to prevent operation hazards and failures.
To overcome the limitation of conventional diagnosis approaches, which misclassify new …
To overcome the limitation of conventional diagnosis approaches, which misclassify new …
Semi-supervised speech activity detection with an application to automatic speaker verification
We propose a simple speech activity detector (SAD) based on recording-specific Gaussian
mixture modeling (GMM) of speech and non-speech frames. We extend the conventional …
mixture modeling (GMM) of speech and non-speech frames. We extend the conventional …
Safe semi-supervised clustering based on Dempster–Shafer evidence theory
In this paper, we propose a safe semi-supervised clustering algorithm based on Dempster–
Shafer (D–S) evidence theory. The motivation is that D–S evidence theory can be used to …
Shafer (D–S) evidence theory. The motivation is that D–S evidence theory can be used to …
Soft fault diagnosis of analog circuits based on semi-supervised support vector machine
L Wang, H Tian, H Zhang - Analog Integrated Circuits and Signal …, 2021 - Springer
Soft fault diagnosis has been validated as a very challenging problem in analog circuits. In
order to improve the generalization ability and close to the practical application of fault …
order to improve the generalization ability and close to the practical application of fault …
Confidence-weighted safe semi-supervised clustering
In this paper, we propose confidence-weighted safe semi-supervised clustering where prior
knowledge is given in the form of class labels. In some applications, some samples may be …
knowledge is given in the form of class labels. In some applications, some samples may be …
Supervised learning of Gaussian mixture models for visual vocabulary generation
The creation of semantically relevant clusters is vital in bag-of-visual words models which
are known to be very successful to achieve image classification tasks. Generally …
are known to be very successful to achieve image classification tasks. Generally …
Joint exploring of risky labeled and unlabeled samples for safe semi-supervised clustering
L Guo, H Gan, S ** similar observations in relatively distinct groups generally known as clusters …
Recent developments in model-based clustering with applications
Abstract Model-based clustering is a popular technique relying on the notion of finite mixture
models that proved to be efficient in modeling heterogeneity in data. The underlying idea is …
models that proved to be efficient in modeling heterogeneity in data. The underlying idea is …