This paper presents a training algorithm for probabilistic neural networks using the MCE criterion.
提出了一种基于最小分类错误准则的概率神经网络的训练算法。
A corollary of this principle is that a learning algorithm should never be evaluated for its results in the training set because this shows no evidence of an ability to generalize to unseen instances.
这个原理的一个推论是，一种学习算法永远不会对它训练集的结果进行评估，因为对于一种未知的事例而言，没有证据表明算法具有概括它们的能力。
This gives you a much larger training set for each trial, meaning that your algorithm will have enough data to learn from, but it also gives a fairly large number of tests (20 instead of 5 or 10).
对每次尝试来说，训练集都非常大，这意味着你的算法有足够的数据进行学习，而且这样一来也提供了足够多的测试次数(20次，而不是5次或10次)。
Feedforward networks use back propagation algorithm to train a multi-layer network. After training, the multi-layer network can fit the function in the data space very well.
前向网络利用反向传播算法训练多层网络，使训练后的网络较好地拟合样本空间中各点的函数值。
A scale training algorithm of BP neural network is used, and sample reorganization method is proposed. Its advantage is the fast training speed and good feature extraction ability.
作者使用比例训练的BP算法，提出对训练模式进行样本重组的方法，其特点是训练速度快、特征抽取能力强。
Training a multilayer neural net by BP algorithm is slow and it is difficult to choose the number of hidden units and layers in advance.
使用BP算法训练多层网络的速度很慢而且事先难于确定隐节点和隐层的适当数目。
This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.
因此，作者将非线性规划的平行切线算法用于神经网络的权值学习，提出了一种具有快速学习算法的神经网络。
A new SVM iterative algorithm is proposed, aiming at the problem that the speeds of learning and classifying are slow in large training set.
针对SVM方法在大样本情况下学习和分类速度慢的问题，提出了大样本情况下的一种新的SVM迭代训练算法。
A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.
提出一种准对角递归神经网络(QDRNN)结构及学习算法。
A maximum entropy model of feature extraction for the gas disaster information is established, with the training algorithm of the maximum entropy presented.
以最大熵原理为基础，建立了最大熵瓦斯灾害信息特征提取模型，提出了最大熵模型的参数训练算法。
This paper presents a new leaning method for radial basis function network, minimum mean entropy difference criterion algorithm is used to get pattern cluster of training sets.
本文对径向基函数网络提出了一种新的学习算法，利用最小均熵差准则对训练样本进行模式聚类。
Based on gradient algorithm and the fundamental approximation of feedforward network, a new supervised comprehensive training mechanism is put forward.
基于梯度算法和前馈网络所具有的普遍近似性质，提出了一种新的监督型多目标系统化训练机制。
After analysis of em algorithm, we presented a new cooperative training algorithm based on incremental learning.
本文在分析了EM算法的基础上，提出了一种新的协同训练算法。
Further, a hybrid BP algorithm with dead interval of error is derived for training the neural classifier in order to increase training speed and classification accuracy.
此外，提出用带输出误差死区的混合BP算法训练神经元分类器，提高了网络学习训练速度和分类准确性。
It is also indicated that current WNN has a poor convergence performance because of adopting the random initialization method and gradient training algorithm of traditional BP NET.
还指出由于当前的连续小波神经网络主要使用传统BP神经网络的随机初始化方法和基于梯度的训练算法，因此存在收敛性差的缺点。
Based on the equivalence between the original training set and the newly added training set, a new algorithm for SVM-based incremental learning was proposed.
基于原训练样本集和新增训练样本集在增量训练中地位等同，提出了一种新的SVM增量学习算法。
An adaptive gradient descent algorithm for training simplified internally recurrent networks (SIRN) is developed and a new method of reconciling nonlinear dynamic data based on SIRN is proposed.
研究了简化型内回归神经网络基于自适应梯度下降法的训练算法，并提出了一种基于简化型内回归神经网络的非线性动态数据校核新方法。
This paper proposes a new global optimization technique in which combines the filled function method and BP algorithm for Training feedforward neural networks.
把填充函数法与BP算法相结合，提出一种训练前向神经网络的混合型全局优化新算法。
Particle Swarm Optimization as a Swarm Intelligence algorithm, has strong global search capability, can be used for training neural network to overcome the defect of BP algorithm.
然而在粒子群优化算法中，早熟现象时有发生，从而制约了算法的性能。
First, a study of a sliding mode controller under on off training is made and then the on line learning algorithm using a gradient decent method is designed.
首先研究了离线训练的滑模控制器，然后，给出了利用梯度下降法的在线训练方法。
A hybrid RBF training method based on immune algorithm and least square method is proposed and applied in radar antenna scanning-style recognition system.
采用一种基于免疫算法和最小二乘法的两级学习方法设计径向基函数（RBF）网络，并将其应用于雷达天线扫描方式识别系统。
The main target of the thesis is: for the shortcoming of the slowly training speed about SVM, we want to find a new SVM accelerated training algorithm based on the existing SVM algorithms.
本文主要的研究工作是：针对支持向量机训练速度慢的问题，在现有支持向量机加速训练算法的基础上，寻找一种新的SVM加速训练算法。
After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training.
经过我选择了多层感知和标准的反向传播训练算法的研究。
This mapping relation is determined by training neural network with a back-propagation algorithm, which is utilized to estimate images at finer resolution from coarser versions.
使用反向传播算法训练神经网络，确定这种映射关系；根据该映射关系由低分辨力图像估计高分辨力图像。
A fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
模糊神经网络的学习算法采用的是快速的粒子群优化算法。
The support vector machine is a learning algorithm, which has a good classification ability for limited training samples.
支撑矢量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
Explores the training problems of support vector machine with large training pattern set, and a new parallel algorithm based on orthogonal array is presented.
对大规模训练样本的支持向量机训练问题进行探索，提出了一种基于正交表的并行学习算法。
A new algorithm is proposed for training single layered perception neural networks.
提出了一种单层感知器网络训练的新算法。
A new algorithm is proposed for training single layered perception neural networks.
提出了一种单层感知器网络训练的新算法。
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