Opt-RNN-DBFSVM:基于最优递归神经网络密度的模糊支持向量机

K. E. Moutaouakil, Abdellatif el Ouissari
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引用次数: 0

摘要

在实现支持向量机时,会遇到两个主要问题:(a)局部极小值的数量随着样本数量呈指数级增长;(b)常规二次规划求解器所需的计算机存储量随着问题规模的扩大呈指数级增长。最近受到关注的Kernel-Adatron算法家族允许处理非常大的分类和回归问题。然而,这些方法以相同的方式处理不同类型的样本(噪声、边界和核心),这会导致在没有希望的区域进行搜索,并增加迭代次数。在这项工作中,我们引入了一种混合方法来克服这些缺点,即基于最优递归神经网络密度的支持向量机(Opt-RNN-DBSVM)。该方法包括四个步骤:(a)表征不同样本,(b)消除低概率成为支持向量的样本,(c)基于原始能量函数构建适当的递归神经网络,以及(d)求解微分方程组,使用涉及最优时间步长的欧拉-柯西方法管理RNN的动力学。由于它的循环结构,RNN会记住在搜索过程中所探索的区域。我们证明了RNN-SVM收敛于可行的支持向量,与恒定时间步长的RNN-SVM和ka - svm相比,Opt-RNN-DBSVM具有非常低的时间复杂度。在学术数据集上进行了几个实验。我们使用几种分类性能指标对Opt-RNN-DBSVM与不同的分类方法进行了比较,结果表明该方法具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Opt-RNN-DBFSVM: Optimal recurrent neural network density based fuzzy support vector machine
When implementing SVMs, two major problems are encountered: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential magnitude as the problem size expands.  The Kernel-Adatron family of algorithms gaining attention lately which has allowed to handle very large classification and regression problems. However, these methods treat different types of samples (Noise, border, and core) with the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work , we introduce a hybrid method to overcome these shortcoming, namely Optimal Recurrent Neural Network Density Based Support Vector Machine (Opt-RNN-DBSVM).  This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler-Cauchy method involving an optimal time step. Thanks to its recurrent architecture, the RNN remembers the regions explored during the search process. We demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has a very low time complexity compared to RNN-SVM with constant time step, and KAs-SVM. Several experiments were performed on academic data sets. We used several classification performance measures to compare Opt-RNN-DBSVM to different classification methods and the results obtained show the good performance of the proposed  method.
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