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separable problem
相关语句
  可分问题
     Support Vector Robust Perceptron for Linear Separable Problem and its Geometric Training Algorithm
     线性可分问题的支撑向量稳健感知器及其几何训练算法
短句来源
     Based on this fact,we propose a Soft Kernel Perceptron(SKP) in terms of L2 norm,in which the regular perceptron is directly employed to solve the linearly separable problem determined by L2 norm soft margin algorithms.
     基于这一事实,提出一种基于L2范数的软核感知机(SoftKernelPerceptron,SKP),将感知机算法直接用于求解L2范数软边缘算法决定的线性可分问题
短句来源
     The linear and non-linear separability of Boolean functions are difficult problems, in which only linearly separable problem for dimension n ≤7 had ever been discussed.
     Boole函数的线性可分和线性不可分问题,一直是前向人工神经网络的一个比较困难的问题,目前仅对变量数n≤7的线性可分问题给予过讨论。
短句来源
     In this model, a self adaptive feature space expanding layer is added in front of the feedforward neural network to enhance the description of the original pattern, thus the nonlinear separable problem can be transformed into linear one or less nonlinear one, and our new model converges faster than the traditional feedforward neural network.
     该模型在标准前馈式神经网络的前端增加一个自适应特征空间扩张层 ,自适应地增强原始模式的表达 ,将原来的非线性可分问题转换成线性可分问题或者减小其非线性程度 ,从而加快网络的收敛速度 .
短句来源
  “separable problem”译为未确定词的双语例句
     But for the continuous perceptrons, despite the excellent application, we have not found satisfactorily proved results for the linearly separable problem. Researchers have also attempted to obtain the convergence of the online BP algorithm for nonlinear multilayer perceptrons.
     但在连续型感知器(单层和多层)中广泛使用的在线BP算法,虽然在实际应用中有了很好的效果,并且有许多学者也已经尝试着得到在线BP算法的收敛性,但是,在解决线性可分这样基本的分类问题方面,在线BP算法还没有取得令人满意的有限收敛性结果.
短句来源
     kernel method has been of wide concern in the field of machine learning recently. It allows the efficient computation of linear classification in high-dimensional feature space, instead of non-linearly separable problem in low-dimensional input space.
     核方法是近年发展起来的一种新的机器学习方法,它可在高维(特征)空间中用线性的方法有效地解决低维(输入)空间中线性不可分问题.
短句来源
  相似匹配句对
     problem.
     潮滩环境问题越来越多元化、复杂化。
短句来源
     On Problem
     问题论
短句来源
     The Additive Edge Problem in Separable Graph
     一类图的可加边问题
短句来源
     Direct algorithm for separable continuous convex quadratic knapsack problem
     求解可分离连续凸二次背包问题的直接算法
短句来源
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  separable problem
The initial values thus obtained are utilized in the formulation of an eigenfunction solution to a non-separable problem in which the derivatives of the solution function are of interest, so that retention of analytic control is desirable.
      
If a satellite orbit is described by means of osculating Jacobi α's and β's of a separable problem, the paper shows that a perturbing forceF makes them vary according to
      
Global convergence is proved for a partitioned BFGS algorithm, when applied on a partially separable problem with a convex decomposition.
      
This integer problem is formulated by a simple piecewise-linear underestimation of the separable problem.
      
The original problem is reduced to an equivalent separable problem by solving a multiple-cost-row linear program with 2n cost rows.
      
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Artificial neural network model is a kind of nonlinear dynamical net system composed of a large scaleof extensively interconnected simple computing elements.For the parallel distributed processing,associa-tive memory,self-organization,self-learning,and strong mapping abilities,it has shown broad applicationprospects in many fields.In this paper,from the view points of pattern recognition,the artificial neuralnetwork technique and its applications to rotating machinery fault diagnosis are discussed,the networktopollogy(i.e.the...

Artificial neural network model is a kind of nonlinear dynamical net system composed of a large scaleof extensively interconnected simple computing elements.For the parallel distributed processing,associa-tive memory,self-organization,self-learning,and strong mapping abilities,it has shown broad applicationprospects in many fields.In this paper,from the view points of pattern recognition,the artificial neuralnetwork technique and its applications to rotating machinery fault diagnosis are discussed,the networktopollogy(i.e.the number of hidden layers and the number of hidden units)and its decision ability to formthe demanded classification regions are also studied.Based on the standard frequency spectrum waveformfeatures which are represented in power ratios of nine different frquency intervals,five types of typicalfaults in rotating machinery are analyzed and diagnosed with the famous perceptron networks by Back-Propagation algorithm.In addition,this kind of adaptive neural network method is compared with the tra-ditional pattern recognition approach.The resrarch results show that artificial neural network techniquehas special pattern classification properties for high dimension and nonlinear pattern recognition problemsbecause of its extensive interconnected nonlinear network architecture and its strong parameter self-learn-ing ability.The clssification ability of the network is a function of the number of hidden layers and hiddennodes.The addition of more hidden nodes improved the learning speed somewhat but the ability of the net-work to recall and generalize suffers.For a linearly separable problem,a single—layer perceptrou networkcan be adopted.For a linear unseparable problem,multi-layer perceptron networks can be adopted.Inpractical application situations,a two-layer perceptron network(one hidden layer)with suitable hiddexnodes can form enough complex decision regions.Then,the number of hidden nodes is determined accord-ing to the problem complexity.As a new pattern recognition approach,artificial neural network techniqueis capable of solving the complex state recognition problems in fault diagnosis.

人工神经元网络模型是由大量的简单计算单元广泛相互联接而成的一个非线性动力学网络系统,它以高度的并行分布式处理、联想记忆、自组织及自学习能力和极强的非线性映射能力,在众多的领域里显示了广阔的应用前景。本文从模式识别的角度,论述了神经元网络技术及其在旋转机械故障诊断中的应用,就神经元网络结构及其所能形成的模式分类决策区域作了较为详尽的阐述,并与传统的模式识别技术作了比较。最后在振动频谱波形特征的基础上,就旋转机械中五种典型故障模式,用感知器网络进行了试验研究和分析。结果表明,人工神经元网络技术对于高维空间模式识别及非线性模式识别问题,具有较强的分类表达能力。作为一种新的自适应模式识别方法,神经元网络技术能够有效地解决故障诊断中较为复杂的状态识别问题。

The linear and non-linear separability of Boolean functions are difficult problems, in which only linearly separable problem for dimension n ≤7 had ever been discussed. This paper, on the bases of classification complexity of n-dimensional Boo1ean functions, presents a concept of to1erantly lineat c1assification of n-dimensional Boolean functions , and discusses some counting properties of n-dimensional hypercubes with the counting results presented' A1l of these are refered as the theoretical preparation...

The linear and non-linear separability of Boolean functions are difficult problems, in which only linearly separable problem for dimension n ≤7 had ever been discussed. This paper, on the bases of classification complexity of n-dimensional Boo1ean functions, presents a concept of to1erantly lineat c1assification of n-dimensional Boolean functions , and discusses some counting properties of n-dimensional hypercubes with the counting results presented' A1l of these are refered as the theoretical preparation for the further discussion of Boolean functions of tolerantly linear separability to be 2.

Boole函数的线性可分和线性不可分问题,一直是前向人工神经网络的一个比较困难的问题,目前仅对变量数n≤7的线性可分问题给予过讨论。本文在文献[1]中所提出的n-维Boole函数分类复杂度定义的基础上,提出了n-维Boole函数容错分类复杂度的概念,并讨论了n-维超立方体的一些计数性质,给出了计数结果,从而为进一步讨论容错分类复杂度为2的Boole函数及其计数问题做了理论上的准备。

The network decomposition and combination algorithm is proposed, with which a nonlinearly separable problem can be decomposed into several linearly separable sub problems, which can be easily realized by sub nets. Then the sub nets are combined to form a network, which can be efficiently trained to solve the nonlinearly separable problem. This algorithm's convergence is proved. Some example studies were carried out. It was shown that this algorithm, which could be used to obtain hidden...

The network decomposition and combination algorithm is proposed, with which a nonlinearly separable problem can be decomposed into several linearly separable sub problems, which can be easily realized by sub nets. Then the sub nets are combined to form a network, which can be efficiently trained to solve the nonlinearly separable problem. This algorithm's convergence is proved. Some example studies were carried out. It was shown that this algorithm, which could be used to obtain hidden objects and determine number of hidden units in the hidden layer, is a very efficient and fast algorithm for training neural networks.

本文介绍了把线性不可分问题分解为一系列线性可分子问题、对线性不可分问题进行求解的网络分解重组算法.还证明了该算法的收敛性.实例研究表明:该算法不仅可以得到神经网络的隐层空间目标和隐层单元数,而且提高了对线性不可分问题的求解速度,因此是一个非常有效的神经网络训练算法.

 
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