Abstract
We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F2-SVM approach is clearly superior to that of fuzzy MLP and fuzzy K-NN architectures.
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Thiel, C., Scherer, S., Schwenker, F. (2007). Fuzzy-Input Fuzzy-Output One-Against-All Support Vector Machines. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_20
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DOI: https://doi.org/10.1007/978-3-540-74829-8_20
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