Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support...Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines.展开更多
A support vector machine （SVM） based multiuser detection （MUD） scheme in code-division multi- ple-access （CDMA） system is proposed. In this scheme, the equivalent support vector （SV） is obtained through a kern...A support vector machine （SVM） based multiuser detection （MUD） scheme in code-division multi- ple-access （CDMA） system is proposed. In this scheme, the equivalent support vector （SV） is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic pro-gramming （QP） procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit er-ror rate （BER） as the standard SVM and is better than minimum mean square error （MMSE） scheme. Meanwhile, it has a low comoutation complexity.展开更多
文摘Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines.
文摘A support vector machine （SVM） based multiuser detection （MUD） scheme in code-division multi- ple-access （CDMA） system is proposed. In this scheme, the equivalent support vector （SV） is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic pro-gramming （QP） procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit er-ror rate （BER） as the standard SVM and is better than minimum mean square error （MMSE） scheme. Meanwhile, it has a low comoutation complexity.
