Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Data mining with sparse grids using simplicial basis functions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computing
Classification of Time Series Utilizing Temporal and Decision Fusion
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Kernel-based classification using quantum mechanics
Pattern Recognition
Classification with sparse grids using simplicial basis functions
Intelligent Data Analysis
Variations of the two-spiral task
Connection Science
Locally centralizing samples for nearest neighbors
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Perceptual relativity-based local hyperplane classification
Neurocomputing
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The main task for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane. This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications, i.e. the spiral coils with time. Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks. This paper describes a fuzzy approach which outperforms previous work in terms of the recognition rate and the speed of recognition. The paper presents the new approach and results with the validation and test sets. The results show that it is possible to solve the spiral problem in a relatively small amount of time with the fuzzy approach (up to 100% correct classification on the validation and test set; 77.2% correct classification with cross-validation using the leave-one-out method).