基于模糊C-均值的相似性特征转换光滑支持向量机

在用于非线性分类的光滑支持向量机（SSVM）模型中，核函数必须满足Mercer's条件，由此限制了核函数的选择范围；并且在面对大规模数据集时，SSVM模型的计算复杂度很高，训练时间长.针对这两点缺陷提出了基于模糊C-均值的相似性特征转换光滑支持向量机模型（SFT-SSVM-FCM）.首先，运用基于相似性的特征转换，使得核函数不需要再满足Mercer's条件，从而拓宽了核函数的选择范围；其次，运用模糊C-均值（FCM）分群技术，将完整的训练数据集划分成若干子簇，分别在每一个子簇上进行已经过相似性特征转换的SSVM模型训练.实验表明：与传统的SVM、SSVM模型及一系列变体模型相比较，该新模型在训练时间、分类精度方面都具有更好的表现.

Smooth Support Vector Machine with Similarity-Based Feature Transformation Technique and Fuzzy C-Means Clustering

We propose a new model called smooth support vector machine with similarity-based feature transformation and fuzzy C-means (FCM) clustering (SFT-SSVM-FCM).When the similarity-based feature transformation technique is applied,Mercer's conditions are no longer required for kernel functions,thus broadening the range of usable kernel functions.We also incorporate the Fuzzy-C means clustering technique to divide a whole dataset into several clusters each of which is used to perform SSVM with similarity-based feature transformation.The experimental results indicate that the proposed model has better performance compared with the conventional SVM and SSVM model as well as some variants in terms of classification accuracy and training time.