The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently owing to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations that lie close to the boundary of the classifier function. However when the number of observations is not very large (small n) but the number of dimensions/features is large (large p), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of a useful fraction of the available features may result in huge data compression. In this paper, we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie in the context of import vector machine (IVM), to select an optimal sub‐dimensional model to build the final classifier with sufficient accuracy.

中文翻译：

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特征导入向量机：具有灵活特征选择的通用分类器。

基于支持向量机（SVM）和其他基于再生内核希尔伯特空间（RKHS）的分类器系统最近因其鲁棒性和泛化能力而备受关注。这里的总主题是通过使用所有可用维在高维空间中基于训练数据构造分类器。SVM通过仅选择一些接近分类器函数边界的观察值来实现巨大的数据压缩。但是，当观察值的数量不是很大（n很小），而维度/特征的数量却很大（p很大时）），那么在分类环境中不必所有可用功能都具有同等的重要性。可能选择可用功能的有用部分可能会导致巨大的数据压缩。在本文中，我们提出了一种算法方法，通过该方法可以选择这样的最佳特征集。简而言之，我们将传统的SVM顺序观察选择策略与顺序特征选择相反。为了实现这一目标，我们在输入向量机（IVM）的背景下修改了Zhu和Hastie提出的解决方案，以选择最佳子维度模型来构建具有足够准确性的最终分类器。