搜索资源列表
ANN_Matlab
- 书籍“Regularization tools for training large feed-forward neural networks using Automatic Differentiation”的源码文件
bys
- 采用贝叶斯正则化算法提高BP网络的推广能力。在本例中，将采用两种训练方法，即L-M优化算法（trainlm)和贝叶斯正则化算法（trainbr),用以训练BP网络，使其能够拟合某一附加有白噪声的正弦样本数据。-The use of Bayesian regularization algorithm for BP network to improve generalization ability. In this case, two types of training methods will b
marq
- ％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of cor
Ho_Kashyap
- Ho—Kashyap线性分类算法（MHKS）采用了支持向量机最大化间隔的思想,利用面向矩阵模式的双边正则化实现线性分类。-Ho-Kashyap linear classification algorithm (MHKS) using support vector machine to maximize the spacing of the idea, using matrix model for bilateral Regularization achieve linear classifica
network
- 正则化方法分类器，模式识别，matlab-Regularization method classifier, pattern recognition, matlab
L1precision
- 使用MAP估计基于L1正则化的DAG，是该领域最顶尖的Kevin Murphy开发的-MAP estimation of DAG based on L1 regularization
BP
- 基于大坝温控的温度预报程序，采用了L-M优化算法和贝叶斯正则化算法，结果良好-Prediction based on the temperature of the dam temperature control program, using the LM optimization algorithm and the Bayesian regularization algorithm, good results
Adaptive-Online-Learning
- 基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and
romp
- 正则化正交匹配追踪算法的函数，用matlab编写，可以求解压缩感知的信号重构问题-Regularization function orthogonal matching pursuit algorithm, using matlab prepared, can solve problems compressed sensing signal reconstruction
LinearRegression
- 利用numpy库实现线性回归 并且带有正则化之后的线性回归处理功能 还有用最小角回归(LARS)算法实现了lasso回归-Numpy library implements the use of linear regression and linear regression with regularization after processing algorithms as well as the minimum angle of regression (LARS) to achieve a la
BP_LM
- 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正 -Bayesian regularization algorithm to improve the generalization ability of BP network. In this example, we use two training methods, namely LM optimization algorithm (trainlm) and Baye
ManifoldRegularization
- 流型正则化解决方案，包含一些例子，应用较为广泛。-Flow pattern regularization solution, including some examples, which has been widely applied.
0-svnn
- 这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Me
正则化
- 深度网络中防至过拟合的情况，可以使用正则化，这里将实现L2正则化(In case of overfitting in deep network, regularization can be used, and L2 regularization is implemented here.)
Chapter04
- 基于tensorflow 的神经网络的损失函数，学习率，正则化，滑动平均等方法(Method of loss function, learning rate, regularization and sliding average of neural network based on tensorflow)
超限学习机理论讲解及编程实现
- 该方法随机给定神经元权值中的输入权值和阈值，然后通过正则化原则计算输出权值，神经网络依然能逼近任意连续系统。(The method gives the input weights and thresholds of neuron weights randomly, and then calculates the output weights by regularization principle. The neural network can still approximate any cont