搜索资源列表
testHogVSM
- 将样本分类为正样本，负样本，难以辨别样本，依次标记为1，-1，-1，通过hog计算各类样本特征，以xml形式写入vsm模型。测试样本时，只需要加载xml文件，计算出测试样本的hog特征，通过预测函数即可得出他属于哪一类别。-The samples are classified into positive samples, negative samples, and the samples are difficult to distinguish. The samples are labeled
fiuqie_v83
- Relief计算分类权重，实现用SDRAM运行nios，同时用SRAM保存摄像头数据，使用matlab实现智能预测控制算法。- Relief computing classification weight, Implemented with SDRAM run nios, while saving camera data SRAM, Use matlab intelligent predictive control algorithm.
nihe
- 模式分类中基于BP神经网络的曲线拟合；根据BP神经网络的模型，调用MATLAB工具箱函数，通过对样本数据进行训练，对测试样本数据进行成功的预测，预测的输出值与实际值之间几乎没有偏差。-Pattern classification of curve fitting based on BP neural network Based on BP neural network model, call MATLAB toolbox function, through the training sampl
joufao_v16
- sar图像去噪的几种新的方法，Relief计算分类权重，阐述了负荷预测的应用研究。- Several new methods sar image denoising, Relief computing classification weight, It describes the application of load forecasting.
MATLAB-Programing
- 本书内容包括: MATLAB快速入门、重新认识矢量 (向量) 化编程、MATLAB处理海量数据、匿名函数类型介绍、嵌套函数类型介绍、积分以及积分方程求解案例、优化及非线性方程 (组) 求解案例、人脸图像压缩与重建案例、有关预测分类的案例、常微分方程 (组) 求解案例、层次分析法及其MATLAB实现、定时器及其应用。-The book includes: MATLAB quick start, the idea of a vector (Vector) programming, MATLAB pr
laopao
- Relief计算分类权重，matlab开发工具箱中的支持向量机，未来线路预测,分析误差。- Relief computing classification weight, matlab development toolbox support vector machine, Future line prediction, error analysis.
python-code-for-Machine-learning
- 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning
pengan
- 阐述了负荷预测的应用研究,Relief计算分类权重，模拟数据分析处理的过程。- It describes the application of load forecasting, Relief computing classification weight, Analog data analysis processing.
444
- 算法流程：选定训练集和测试集-数据预处理-交叉验证选择最佳参数-分类准确率-预测-利用最佳参数训练SVM-Algorithm flow: selected training set and test set- data preprocessing- cross-validation selection of the best parameters- classification accuracy- prediction- training SVM using the best parameter
555
- 利用SVM算法进行单层竞争神经网络的数据分类患者癌症发病预测-Prediction of Cancer Incidence in Patients with Single- Level Competitive Neural Network Classification Using SVM Algorithm
code
- 人脸识别 FA和BP网络的预测 LVQ神经网络的预测——人脸识别 SVM分类与回归 Elman神经网络预测-Face recognition, FA and BP network prediction, LVQ neural network prediction- face recognition, SVM classification and regression, Elman neural network prediction
miuhai_v68
- Relief计算分类权重，针对EMD方法的不足,未来线路预测,分析误差。- Relief computing classification weight, For lack of EMD, Future line prediction, error analysis.
bangfing
- 可以实现模式识别领域的数据的分类及回归，使用高阶累积量对MPSK信号进行调制识别，阐述了负荷预测的应用研究。- You can achieve data classification and regression pattern recognition, Using high-order cumulants of MPSK signal modulation recognition, It describes the application of load forecasting.
mylinear
- 基于liblinear的预测代码，可进行多类别分类-prediction code based liblinear
tangfou_v70
- 未来线路预测,分析误差,包括主成分分析、因子分析、贝叶斯分析，可以实现模式识别领域的数据的分类及回归。- Future line prediction, error analysis, Including principal component analysis, factor analysis, Bayesian analysis, You can achieve data classification and regression pattern recognition.
MELPcodepaper
- 目前2.4kbps的混合激励线性预测 (MELP) 语音编码方法已经被确定为美国新的联邦语音编码标准.本文提出了一种改进的MELP语音编码方法,利用滤波器相似度和基于LPC系数分类的矢量量化技术,可以把MELP的码率降到1.7kbps以下,仍有较好的合成语音质量.-An Improved MELP Speech Coder
clustering
- c++通过聚类算法实现分类和预测，是比较基础，易懂的算法。-Through clustering algorithm to realize the classification and prediction, c++ is a basic and simple algorithm.
svmtrain
- svm的样本训练代码，用于分类样本数据并进行数值预测的程序-The sample program svm training program, sample data for classification and numerical prediction
SVM-yindao
- 使用Opencv C++中的支持向量机进行引导，对其进行分类并预测。-Opencv C++ using the support vector machine to guide the classification and prediction.
20170106RF_Matlab
- 随机森林指的是利用多棵树对样本进行训练并预测的一种分类器，包括两个方面：数据的随机性选取，以及待选特征的随机选取。-Random forest refers to the use of more than one tree to sample the training and prediction of a classifier, including two aspects: random selection of data, as well as the characteristics of