Survey on multi-output learning
The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …
It is an important learning problem for decision-making since making decisions in the real …
Research progress on semi-supervised clustering
Y Qin, S Ding, L Wang, Y Wang - Cognitive Computation, 2019 - Springer
Semi-supervised clustering is a new learning method which combines semi-supervised
learning (SSL) and cluster analysis. It is widely valued and applied to machine learning …
learning (SSL) and cluster analysis. It is widely valued and applied to machine learning …
Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is
often introduced to an objective function to improve the robustness of the FCM algorithm for …
often introduced to an objective function to improve the robustness of the FCM algorithm for …
von mises-fisher mixture model-based deep learning: Application to face verification
MA Hasnat, J Bohné, J Milgram, S Gentric… - arxiv preprint arxiv …, 2017 - arxiv.org
A number of pattern recognition tasks,\textit {eg}, face verification, can be boiled down to
classification or clustering of unit length directional feature vectors whose distance can be …
classification or clustering of unit length directional feature vectors whose distance can be …
A novel fuzzy clustering based method for image segmentation in RGB-D images
Automatic image segmentation is a challenging task in computer vision applications,
especially in the presence of occluded objects, varying color, and different lighting …
especially in the presence of occluded objects, varying color, and different lighting …
Accurate classification for automatic vehicle-type recognition based on ensemble classifiers
In this paper, a real-world problem of the vehicle-type classification for automatic toll
collection (ATC) is considered. This problem is very challenging because any loss of …
collection (ATC) is considered. This problem is very challenging because any loss of …
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 …
Bottom-up unsupervised image segmentation using FC-Dense u-net based deep representation clustering and multidimensional feature fusion based region merging
Recent advances in system resources provide ease in the applicability of deep learning
approaches in computer vision. In this paper, we propose a deep learning-based …
approaches in computer vision. In this paper, we propose a deep learning-based …
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 …
Bridging spherical mixture distributions and word semantic knowledge for Neural Topic Modeling
Abstract Neural Topic Modeling has attracted significant attention from the Natural
Language Processing community due to its black-box inference property and has made …
Language Processing community due to its black-box inference property and has made …