搜索资源列表
C45Rule-PANE
- Descr iption: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagn
icsiboost-0.3.tar
- Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the
OCD--code
- 通过对集成误差公式的理论分析,提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解,使个体网络的训练准则函数中包含个体网络误差相关度的因素,并通过协同训练,引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明,该方法的故障诊断准确率优于传统的三比值法与BP神经网络,其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。-A learning algorithm is proposed in this paper by
ADL-code
- 通过对集成误差公式的理论分析,提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解,使个体网络的训练准则函数中包含个体网络误差相关度的因素,并通过协同训练,引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明,该方法的故障诊断准确率优于传统的三比值法与BP神经网络,其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。-A learning algorithm is proposed in this paper by
miltool
- 多示例学习的算法集合,集成了各种主流设计算法-An ensemble of the typical multiple-instance learning algorithms
ENtool
- the toolbox contains a good functions for ensemble learning.
robustlssvm
- 鲁棒最小二乘双支持向量机集成学习算法,对于初学者的理解应用特别好用-Robust least squares double support vector machine ensemble learning algorithm for beginners to understand the application of special good use
demo3
- 在demo中,用EKF和有噪声的EKF训练非线性、非平稳数据。-In this demo, I use the EKF and EKF with noise adaptation to train a neural network with data generated a nonlinear, non-stationary state space model. Adaptation is done by matching the innovations ensemble covariance
CNTK
- 在深度的重要性的驱使下,出现了一个新的问题:训练一个更好的网络是否和堆叠更多的层一样简单呢?解决这一问题的障碍便是困扰人们很久的梯度消失/梯度爆炸,这从一开始便阻碍了模型的收敛。归一初始化(normalized initialization)和中间归一化(intermediate normalization)在很大程度上解决了这一问题,它使得数十层的网络在反向传播的随机梯度下降(SGD)上能够收敛。 当深层网络能够收敛时,一个退化问题又出现了:随着网络深度的增加,准确率达到饱和(不足为奇)然后迅
boosting_demo
- boosting算法用于集成学习,包含多种弱分类器(Boosting algorithm is used for ensemble learning, and it contains many weak classifiers)
arimanet
- ARIMA模型全称为自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA),是由博克思(Box)和詹金斯(Jenkins)于70年代初提出一著名时间序列预测方法[1] ,所以又称为box-jenkins模型、博克思-詹金斯法。其中ARIMA(p,d,q)称为差分自回归移动平均模型,AR是自回归, p为自回归项; MA为移动平均,q为移动平均项数,d为时间序列成为平稳时所做的差分次数。所谓ARIMA模型,是指将非平稳