A Study on Machine Learning with Radial Basis Function Networks

• Main Authors: Yu-Yen Ou, 歐昱言
• Other Authors: Yen-Jen Oyang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/41151026801159328196

博士 === 國立臺灣大學 === 資訊工程學研究所 === 93 === This thesis reports a series of studies on machine learning with the radial basis function network (RBFN). The first part of this thesis discusses how to construct an RBFN efficiently with the regularization procedure. In fact, construction of an RBFN with the regularization procedure involves two main issues. The first issue concerns the number of hidden nodes to be incorporated and where the centers of the associated kernel functions should be located. The second issue concerns how the links between the hidden layer and the output layer should be weighted. For the first issue, this thesis discusses the effects with a random samples based approach and an incremental clustering based approach. For the second issue, this thesis elaborates the effects with the Cholesky decomposition employed. Experimental results show that an RBFN constructed with the approaches proposed in this thesis is able to deliver the same level of classification accuracy as the SVM and offers several important advantages. Finally, this thesis reports the experimental results with the QuickRBF package, which has been developed based on the approaches proposed in this thesis, applied to bioinformatics problems. The second study reported in this thesis concerns how the novel relaxed variable kernel density estimation (RVKDE) algorithm that our research team has recently proposed performs in data classification applications. The experimental results reveal that the classifier configured with the RVKDE algorithm is capable of delivering the same level of accuracy as the SVM, while enjoying some advantages in comparison with the SVM. In particular, the time complexity for construction of a classifier with the RVKDE algorithm is O(nlogn), where n is the number of samples in the training data set. This means that it is highly efficient to construct a classifier with the RVKDE algorithm, in comparison with the SVM algorithm. Furthermore, the RVKDE based classifier is able to carry out data classification with more than two classes of samples in one single run. In other words, it does not need to invoke mechanisms such as one-against-one or one-against-all for handling data sets with more than two classes of samples. The successful experiences with the RVKDE algorithm in data classification applications then motivate the study presented next in this thesis. In Section 4.3, a RVKDE based data reduction approach for expediting the model selection process of the SVM is described. Experimental results show that, in comparison with the existing approaches, the data reduction based approach proposed in this thesis is able to expedite the model selection process by a larger degree and cause a smaller degradation of prediction accuracy.