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Application of artificial intelligence in diagnosis of craniopharyngioma
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-
pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other …
pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other …
Adaptive safety-aware semi-supervised clustering
Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
Safety-aware graph-based semi-supervised learning
In machine learning field, Graph-based Semi-Supervised Learning (GSSL) has recently
attracted much attention and many researchers have proposed a number of different …
attracted much attention and many researchers have proposed a number of different …
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 …
Local homogeneous consistent safe semi-supervised clustering
H Gan, Y Fan, Z Luo, Q Zhang - Expert Systems with Applications, 2018 - Elsevier
Semi-supervised clustering generally assumes that prior knowledge is helpful to improve
clustering performance. However, the prior knowledge may degenerate the clustering …
clustering performance. However, the prior knowledge may degenerate the clustering …
Safe semi-supervised extreme learning machine for EEG signal classification
One major challenge in the current brain–computer interface research is the accurate
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
On using supervised clustering analysis to improve classification performance
During the past decade, graph-based learning methods have proved to be an effective tool
to make full use of both labeled and unlabeled data samples to improve learning …
to make full use of both labeled and unlabeled data samples to improve learning …
Stratification-based semi-supervised clustering algorithm for arbitrary shaped datasets
F Wang, L Li, Z Liu - Information Sciences, 2023 - Elsevier
Semi-supervised clustering is not only an important branch of semi-supervised learning but
also an improvement direction for clustering. Semi-supervised clustering algorithms …
also an improvement direction for clustering. Semi-supervised clustering algorithms …
Safe Semi-Supervised Fuzzy -Means Clustering
H Gan - IEEE Access, 2019 - ieeexplore.ieee.org
With the rapid increase in the number of collected data samples, semi-supervised clustering
(SSC) has become a useful mining tool to find an intrinsic data structure with the help of prior …
(SSC) has become a useful mining tool to find an intrinsic data structure with the help of prior …