Unconstrained Functional Networks Classifier
نویسنده
چکیده
This paper proposes unconstrained functional networks classifier as a novel approach for solving pattern classification problems. Both initial topology and learning methodology of the new unconstrained functional networks classifier are presented. The performance of the new networks classifier is examined using both real-data and simulation studies. A comparative study with the most common classification algorithms is carried out. The new functional networks classifier performs better. The results show its stable and quality performance..
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