Incremental Possibilistic Approach for Online Clustering and Classification
Explore this paper's citation graph
Summary
This paper proposes to develop the supervised classification method Fuzzy Pattern Matching to be in addition a non supervised one to monitor dynamic systems with a limited prior knowledge about their functioning.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 0
- References
- 17
- OpenAlex
- https://openalex.org/W166628796
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7820884
Keywords
Artificial intelligence, Computer science, Cluster analysis, Machine learning, Supervised learning
References
- Some new indexes of cluster validity
- A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
- A process monitoring module based on fuzzy logic and pattern recognition
- Incremental learning in Fuzzy Pattern Matching
- Fuzzy sets as a basis for a theory of possibility
- FUZZY PATTERN MATCHING
- Toward integrating feature selection algorithms for classification and clustering
- BOOK REVIEW: "PATTERN CLASSIFICATION", R. O. DUDA, P. E. HART and D. G. STORK, Second Edition
- Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
- Combining labelled and unlabelled data in the design of pattern classification systems
- Feature extraction for multisource data classification with artificial neural networks
- The ACL Anthology Network Corpus as a Resource for NLP-based Bibliometrics
- Adaptive fuzzy monitoring and fault detection
- Semi-Supervised Clustering Using Genetic Algorithms
- On Possibility/Probability Transformations
- Advances in statistical pattern recognition
Cited by
No citing papers recorded for this paper.
Related papers
- Using clustering analysis to improve semi-supervised classification
- Track-based self-supervised classification of dynamic obstacles
- Automatic labeling by means of semi-supervised fuzzy clustering as a boosting mechanism in the generation of fuzzy rules
- Topological Kernel Bayesian ARTMAP
- New approach for systems monitoring based on semi-supervised classification
- Semi-supervised learning with an imperfect supervisor
- A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms
- Efficient supervised learning with reduced training exemplars