Difference between revisions of "Machine learning"
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(Created page with " Machine learning is the core of knowledge extraction, of data science, and of artificial intelligence. Our team works on three main topics. =Clustering= Our works on clust...") |
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Our team works on three main topics. | Our team works on three main topics. | ||
− | =Clustering= | + | ==Clustering== |
Our works on clustering focus on collaborative clustering, on one hand, and on constrained, semi-supervised clustering, on the other hand. | Our works on clustering focus on collaborative clustering, on one hand, and on constrained, semi-supervised clustering, on the other hand. | ||
− | =Formal concept analysis= | + | ==Formal concept analysis== |
We work on three axes of formal concept analysis: | We work on three axes of formal concept analysis: | ||
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* Adaptation to temporal sequences and graphs | * Adaptation to temporal sequences and graphs | ||
− | = Supervised learning and transfer learning= | + | == Supervised learning and transfer learning== |
We study deep learning for image segmentation and classification, and object detection. | We study deep learning for image segmentation and classification, and object detection. | ||
We work on model reuse, imbalanced data, and active learning. | We work on model reuse, imbalanced data, and active learning. |
Latest revision as of 21:45, 2 June 2021
Machine learning is the core of knowledge extraction, of data science, and of artificial intelligence.
Our team works on three main topics.
Clustering
Our works on clustering focus on collaborative clustering, on one hand, and on constrained, semi-supervised clustering, on the other hand.
Formal concept analysis
We work on three axes of formal concept analysis:
- relational concept analysis (RCA)
- Pattern/rule extraction
- Adaptation to temporal sequences and graphs
Supervised learning and transfer learning
We study deep learning for image segmentation and classification, and object detection.
We work on model reuse, imbalanced data, and active learning.