This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in Rn. In each iteration, a subset of the sampling data (n-points) is adaptively chosen and a hyperplane is constructed such that it separates the n-points at a margin ∈ and it best classifies the remaining points. The classification problem is formulated and three different algorithms are presented. Numerical results show that few iterations are sufficient for convergence. Further, the algorithm is extended to solve quadratically separable classification problems. The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.
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机译：本文介绍了一种快速自适应迭代算法，可以解决R N 中线性可分离分类问题。在每次迭代中，自适应地选择采样数据（n点）的子集，并且构建超平面使得它在边缘处将n点分开∈并且它最能分类剩余点。配制分类问题，并呈现了三种不同的算法。数值结果表明，很少有迭代足以收敛。此外，算法扩展以解决二次可分离的分类问题。基本思想基于将物理空间映射到另一个更大的位置，其中问题变得线性可分离。
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