Study On Some Issues Of Kernel Machine Learning Method

This paper dealed with machine learning, kernel machine method, wavelet kernel machine and fuzzy wavelet kernel machine technology detailedly. Several kinds of kernel machine models were constructed, and have been applied to non-stationary signals processing. Theoretical analysis and implementation results show their validity and feasibility. Some important issues have been discussed in this paper as follows.It is well known that general SVM (Support Vector Machine) costs too much time on large scale data sets. As a valid solution, support vectors pre-extraction method has been discussed in this paper. Kernel perceptron firstly has been used to extract quasi-support vectors. And then, quasi-support vectors were input to standard SVM to process accurately. This method takes advantages of the high speed of perceptron for its simple iterative structure. Perceptron costs fewer time than general SVM, especially on large scale data sets, and can control number of quasi-support vectors easily by a threshold variable, much time will be economized in latter process. Some special technologies, such as kernel function and error boundary, etc, have been adopted to conduct linear separable, nonlinear separable, nonlinear unseparated model recognition and regression questions effectively.Generally, imperceptible features are very important in non-stationary signal processing. Some kinds of complex methods were discussed in this paper, which combined wavelet, Principal Component Analysis (PCA) and kernel function teconology. Wavelet kernel function was constructed after proofs of propositions, that it can meet Mercy condition needs and has reproduction feature in Hilbert space. A kind of kernel machine model was presented and some numerical simulation experiments were applied to validate its correctness. Experiment results show that complex Gaussian wavelet kernel almost has the approximate performance as complex Morlet wavelet kernel, however, excel than general Gaussian kernel and polynomial kernel.Fuzzy and wavelet technology were adopted to construct a kind of fuzzy wavelet kernel function. After that, a kind of kernel machine model based on SVM was built and proofs of consistant approximation were shown immediately. Based upon these theories foundation, Fuzzy Wavelet Support Vector Kernel Machine (FW-SVKR) was formed. The close relationship between parameters and forecastresults was expatiated later. Contrast experiments between FW-SVKR and Artificial Neurial Network (ANN) show that the former was superior to the latter in electric power system load forcasting, and seems to have more applied value in this domain.Theory analysis suggested that much more time should be cost when training a multi-parameters model, which formed the mainly obstacle in application. Aimed at this question, a kind of new technology named multi-parametes synchronous optimization method was proposed. It can save a lot of time when training parameters, and enhance applied value remarkably. Experiment results show its advantages in application.Along with the boost of country economy, city traffic block should be solved urgently. Based on analysis of features of city traffic flow, some kind of kernels such as general kernels, compound kernels and fuzzy wavelet kernels, have been adopted to do realtime traffic flow forecast. Contrast experiment results show the different performance of those kernels, which can help to improve the city traffic control power effectively. Another application of kernel machine method was discussed in this paper, complex kernel function method and support vectors preextraction technology have been adopted to retrieve the Oceanic Chlorophyll-a Concentration in SeaWIFS data sets. Further more, ANN altorithm and twelve kinds of empirical algorithms have been adopted, too. Contrast experiment results show that, retrieve precision with complex kernel function method is higher than that of other algorithms;it seems to be more suitable in this domain.