Similarity Based Classification
Similarity Based Classification
复制标题
基于相似性的分类
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
J. Lassez
中科院分区：
文献类型：
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作者：
A. Bernal;Karen Hospevian;Tayfun Karadeniz;J. Lassez
我们描述了数据分类的一般条件，这些条件可以作为基于核的机器学习算法研究的统一框架。根据这些条件，我们推导出一种新的算法，称为SBC（基于相似性的分类），它在欠拟合，过拟合，泛化能力，计算复杂性和鲁棒性方面具有吸引人的理论特性。与经典算法（如Parzen窗口和非线性感知器）相比，SBC可以被视为它们的优化版本。最后，对于任意数量的类，它是一种概念上更简单、更有效的替代支持向量机。通过若干基准分类问题说明了其实际意义。
We describe general conditions for data classification which can serve as a unifying framework in the study of kernel based Machine Learning Algorithms. From these conditions we derive a new algorithm called SBC (for Similarity Based Classification), which has attractive theoretical properties regarding underfitting, overfitting, power of generalization, computational complexity and robustness. Compared to classical algorithms, such as Parzen windows and non-linear Perceptrons, SBC can be seen as an optimized version of them. Finally it is a conceptually simpler and a more efficient alternative to Support Vector Machines for an arbitrary number of classes. Its practical significance is illustrated through a number of benchmark classification problems.
DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D