搜索资源列表
AMLALL
- cross validation with neurel network methods
refpaper6_hcrnumkannada
- Abstract. This paper describes a system for isolated Kannada handwritten numerals recognition using image fusion method. Several digital images corresponding to each handwritten numeral are fused to generate patterns, which are stored in 8x8 ma
Custom-Evaluation
- 提出一种基于粗糙集与支持向量机的客户动态评估方法。根据客户群特点从当前价值、潜在价值和附加价值三个维度分析并构建客户评估指标,利用指标的年增幅率监测客户价值的变化规律。应用粗糙集布尔推理算法、粒子群算法实现连续属性离散化和知识约简。通过10-重交叉验证和网格搜索技术获取最优惩罚因子与核参数,缩放样本数据集并完成支持向量机一对一分类器的训练与测试。结果表明该评估方法能够实现周期性的客户价值评估与细分,具有很强的泛化能力。- A customer dynamic evaluation method
Grnn-neural-network--Matlab
- Grnn神经网络交叉验证,matlab中可实现代码文档-Grnn nerve network cross-validation, matlab in the can be to achieve the code documentation
libsvm-3.17.tar
- 该软件对SVM所涉及的参数调节相对比较少,提供了很多的默认参数,利用这些默认参数可以解决很多问题;并提供了交互检验(Cross Validation)的功能。-The software adjust the parameters of SVM involved is relatively small, a lot of the default parameters, these default parameters can solve many problems cross-validatio
CrossValidation
- 介绍了交叉验证的基本概念和常见问题。不是源码,但是编程用对新手很有用。-Introduces the basic concepts of cross-validation and frequently asked questions. Not the source, but the programming is useful for the novice to use.
cross-validation
- 介绍了交叉验证的基本概念和常见问题。不是源码,但是编程用对新手很有用。-Introduces the basic concepts of cross-validation and frequently asked questions. Not the source, but the programming is useful for the novice to use.
the-maximum-likelihood-estimate
- 1、 极大似然估计 尝试用0~24阶多项式拟合,并用5折交叉验证选择最佳模型(多项式阶数及其系数,给出类似课件中的图),并画出最佳模型的拟合效果图(类似图1,蓝色点为训练样本、红色点为测试样本、绿色线为模型预测),给出该模型的测试误差。 2、 岭回归 多项式阶数为24,正则系数λ的取值范围为exp(-19)到exp(20),采用并用5折交叉验证选择最佳模型。实验结果要求同1。 -1, the maximum likelihood estimate of 0 to 24 try-o
ionosphere1
- ionosphere ten fold cross validation set (part 1)
ionosphere2
- ionosphere ten fold cross validation set (part 2)
ecoli1
- ecoli ten fold cross validation set (part 1)
ecoli2
- ecoli ten fold cross validation set (part 2)
10折交叉验证(神经网络)
- matlab进行十折交叉验证神经网络,用于预测(Matlab performs ten fold cross validation using neural networks)
trainnet_cross
- 采用10折交叉验证得到光合速率预测模型,代码中有程序语言的详细注释。(The 90% off cross validation of photosynthetic rate prediction model, the procedures detailed notes in the language code.)