Title :
Fuzzy kernel perceptron
Author :
Chen, Jiun-Hung ; Chen, Chu-Song
Author_Institution :
Inst. of Inf. Sci., Acad. Sinica, Taipei, Taiwan
fDate :
11/1/2002 12:00:00 AM
Abstract :
A new learning method, the fuzzy kernel perceptron (FKP), in which the fuzzy perceptron (FP) and the Mercer kernels are incorporated, is proposed in this paper. The proposed method first maps the input data into a high-dimensional feature space using some implicit mapping functions. Then, the FP is adopted to find a linear separating hyperplane in the high-dimensional feature space. Compared with the FP, the FKP is more suitable for solving the linearly nonseparable problems. In addition, it is also more efficient than the kernel perceptron (KP). Experimental results show that the FKP has better classification performance than FP, KP, and the support vector machine.
Keywords :
fuzzy neural nets; learning (artificial intelligence); pattern classification; perceptrons; Mercer kernel; fuzzy perceptron; high-dimensional feature space; kernel-based method; learning method; mapping functions; pattern classification; supervised learning; support vector machine; Constraint optimization; Data mining; Kernel; Learning systems; Pattern classification; Principal component analysis; Quadratic programming; Supervised learning; Support vector machine classification; Support vector machines;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.804311
