Advances in statistical pattern recognition
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
AI Game Programming Wisdom
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network
Journal of VLSI Signal Processing Systems
Incremental learning in fuzzy pattern matching
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
A Semi-Supervised Learning Method for Remote Sensing Data Mining
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Semi-supervised learning for semantic parsing using support vector machines
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Clustering by competitive agglomeration
Pattern Recognition
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A new neural network for cluster-detection-and-labeling
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Estimation of the number of clusters using heterogeneous multiple classifier system
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Drift detection and characterization for fault diagnosis and prognosis of dynamical systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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The behaviour of a dynamic system can assume different operating states in the course of time. In pattern recognition methods, each state is represented by a set of similar patterns forming restricted regions in the feature space, called classes. Recognising the state, class, of a new incoming pattern can be performed using a membership function. The membership function accuracy depends on the prior knowledge about the system functioning. For dynamic systems, this knowledge often suffers from two drawbacks. Firstly, there is no prior information about some states. Thus, the occurrence of new states must be detected and integrated online in the data set. Secondly, the prior information about some states, especially the faulty ones, is not sufficient to properly estimate their membership functions. The missing information can be obtained from the new classified patterns. Thus, these membership functions must be adapted online with the classification of new incoming patterns. In this paper, we propose a semi-supervised classification method based on fuzzy pattern matching (FPM). The goal is to learn membership functions with a limited initial data set. The class membership function according to each feature is sequentially learned with the occurrence of patterns and then it is updated online using an incremental or recursive approach. This learning method does not require any prior information about the nature of classes or their number.