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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Perpendicular Bisector Constraint on Artificial Neural Network
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Perpendicular Bisector Constraint on Artificial Neural Network

机译：人工神经网络的垂直二分约束

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A perpendicular bisector solution of perceptron and a perpendicular bisector constraint on artificial neural network(ANN) are studied in this paper. As is known, both Preceptron and multilayer ANN based on back-propagation algorithm suffer from similar issues of solution instability. The classical SVM can solve the solution instability of perceptron, yet in low efficiency. We use perpendicular bisector of two center vectors, which are composed of weighted positive and negative sample points respectively, as the solution of the perceptron(called PBP). The solution of PBP is stable and more efficient than SVM because of the uniqueness and stability of perpendicular bisector. Then we apply the perpendicular bisector as a constraint(PBC) for multilayer ANN. The solution of constrained ANN should near the perpendicular bisector, thus we can get a more stable solution compared with the one does not have the constraint. The main contributions are: 1. Put forward PBP as a new solver for perceptron with solution stability and less running time than SVM. For example, on data sets Heart Scale and Abalone, training speeds of PBP are at least 10 times faster than SVM. 2. The ANN with PBC improves the solution stability. For an instance, on the highly linear inseparable dataset Abalone, ANN with PBC keeps good convergence property while the one without PBC always oscillates.
机译：研究了感知器的垂直平分线解和人工神经网络上的垂直平分线约束。众所周知，基于反向传播算法的Preceptron和多层ANN都存在解决方案不稳定性的类似问题。经典的SVM可以解决感知器的解决方案不稳定性，但效率低下。我们使用两个中心向量的垂直平分线作为感知器的解（称为PBP），两个中心向量分别由加权的正样本点和负样本点组成。由于垂直平分线的独特性和稳定性，PBP的解决方案比SVM稳定且有效。然后，我们将垂直平分线作为多层ANN的约束（PBC）。约束ANN的解应该在垂直平分线附近，因此与没有约束的解相比，我们可以得到一种更稳定的解。主要的贡献是：1.提出了PBP作为感知器的新求解器，其解决方案稳定性和运行时间少于SVM。例如，在心脏量表和鲍鱼数据集上，PBP的训练速度至少比SVM快10倍。 2.具有PBC的ANN可提高解的稳定性。例如，在高度线性不可分割的数据集鲍鱼上，具有PBC的ANN保持良好的收敛性，而没有PBC的ANN总是振荡。

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