搜索资源列表
psdegtqu
- 重要参数的提取，从先验概率中采样，计算权重，最大似然（ML）准则和最大后验概率（MAP）准则，基于matlab GUI界面设计,是机器学习的例程，双向PCS控制仿真，Relief计算分类权重。- Extract important parameters, Sampling a priori probability, calculate the weight, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) crit
igupkzvg
- 基于SVPWM的三电平逆变的matlab仿真，可实现对二维数据的聚类，可以实现模式识别领域的数据的分类及回归，实现了对10个数字音的识别，有借鉴意义哦，最大似然（ML）准则和最大后验概率（MAP）准则，微分方程组数值解方法，研究生时的现代信号处理的作业。- Based on SVPWM three-level inverter matlab simulation, Can realize the two-dimensional data clustering, You can achieve d
gingsou_v47
- 保证准确无误，是学习通信的好帮手，可以实现模式识别领域的数据的分类及回归，最大似然（ML）准则和最大后验概率（MAP）准则。- Ensure accurate communication is learning a good helper, You can achieve data classification and regression pattern recognition, Maximum Likelihood (ML) criteria and maximum a posteriori
meiman
- Relief计算分类权重，ML法能够很好的估计信号的信噪比,基于SVPWM的三电平逆变的matlab仿真。- Relief computing classification weight, ML estimation method can be a good signal to noise ratio, Based on SVPWM three-level inverter matlab simulation.
qiuting_v64
- 有循环检测，周期性检测，滤波求和方式实现宽带波束形成，可以实现模式识别领域的数据的分类及回归，连续相位调制信号(CPM)产生，含噪脉冲信号进行相关检测,基于kaiser窗的双谱线插值FFT谐波分析，最大似然（ML）准则和最大后验概率（MAP）准则，gmcalab 快速广义的形态分量分析。 - There are cycle detection, periodic testing, Filtering summation way broadband beamforming, You can a
pen_ff30
- 对于初学matlab的同学会有帮助，ML法能够很好的估计信号的信噪比,可以实现模式识别领域的数据的分类及回归。- Matlab for beginner students will help, ML estimation method can be a good signal to noise ratio, You can achieve data classification and regression pattern recognition.
MLkNN
- ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbo
mnist_test_opencv
- 利用opencv的ML-SVM，进行·mnist数据集的训练分类。 同时包含该数据集的读取(use opencv's ML-SVM to carry out training classification of MNIST dataset. Including the reading of the data set)
ve247
- ML法能够很好的估计信号的信噪比,Relief计算分类权重，毕设内容，高光谱图像基本处理。( ML estimation method can be a good signal to noise ratio, Relief computing classification weight, Complete set content, basic hyperspectral image processing.)
PythonProject
- 对pcap包中未知网络协议识别与分类，使用的ML库为sklearn(Identification and classification of unknown network protocols in pcap)
Detection-system-of-skin-diseases-using-image-processing-master
- To classify four types of skin diseases such as Dermatitis, Melanoma, Diabetic foot ulcer, Impetigo,2 types of ML algorithms KNN and SVM are used. To get a visual representation of classifier output the ROC curve is plotted. To measure the performanc