Wavelet support vector machine

By Li Zhang, Weida Zhou, L. Jiao. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004

533 citations recorded by the source corpus.

An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis. Computer simulations show the feasibility and validity of wavelet support vector machines (WSVMs) in regression and pattern recognition.

Subfields: Analytical Chemistry, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0848ad52c4e3cbddb78049b42b75afe25112b26d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. K. Hasan
2. M. J. Alam
3. A. Nury

In Artificial Intelligence

1. K. Hasan
2. M. J. Alam
3. A. Nury
4. Qi Feng
5. Xiaohu Wen
6. Zhi-bin He
7. Jianhua Si
8. Lin-shan Yang

In Computer Vision and Pattern Recognition

1. K. Hasan
2. M. J. Alam
3. A. Nury
4. Pei-Chann Chang
5. Xiaohu Wen
6. Zhe Yan
7. Li Zhang
8. Ting Wang

Citations in the corpus

• Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh (2017, methodology): “Kisi and Cimen (2011) used three decomposition levels in their monthly stream-flow forecasting study.”
• Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition (2012, background): “The multidimensional wavelet function can be defined as [33]”
• State-of-the-art review of soft computing applications in underground excavations (2020, methodology): “An SVM uses a device called kernel, such as the Gaussian and polynomial kernels, to map data into a high-dimensional feature space in which the nonlinear problem becomes linearly separable (Zhang et al., 2004).”
• Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “To improve the modeling accuracy of the SVR model, Guo et al. (2011) and Kisi and Cimen (2011) both combined the wavelet and the SVR model in river flow forecasting problems where the two results both revealed that the improved SVR model combined with wavelet analysis can forecast hydrological data…”
• Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, methodology): “Zhang et al. (2004) developed a wavelet SVM that employs a wavelet kernel to approximate arbitrary nonlinear functions.”
• Kernel Sparse Representation-Based Classifier (2012, background): “Assume that there are a set of training samples , where is the number of classes, is the dimensionality of the input space , and is label corresponding to .”