搜索资源列表
Classification-MatLab-Toolbox
- 模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值-pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
pattern_recognition_v6.1
- 完整的模式识别库，包括矩阵运算，各种模式识别算法，如K均值、SVM、RVM、NN、LDA等
facedetector
- 人脸检测源代码. The souce demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized SVM and NN rough face prefiltering, PCA/LDA/ICA/any dimensionality reduct
模式识别matlab工具箱,包括svm,ICA,PCA,NN等等模式识别算法
- 模式识别matlab工具箱,包括svm,ICA,PCA,NN等等模式识别算法
prtools3.1.7.rar
- 模式识别 MATLAB 的工具箱,比较实用,包括SVM,ICA,PCA,NN等等模式识别算法.,Pattern Recognition Toolbox for MATLAB, more practical, including the SVM, ICA, PCA, NN pattern recognition algorithm and so on.
libsvm-2.89
- 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEA
DetectionLibrary
- Chesnokov Yuriy写的人脸检测库，内容涵盖肤色检测、运动估计、SVM分类、PCA/LDA/ICA特征提取以及神经网络分类器等。-The article demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized SVM and NN
webcat
- 这是一个100 ％纯Java库，您可以使用适用于N元 分析技术的过程分为文本文件。 该计划包括几个不同的分类算法， namelly 支持向量机，贝叶斯Logistic回归，神经网络分类和文本压缩 算法。如支持向量机和贝叶斯Logistic回归，一个 “一对一” 用于多类分类。更详细的说明这些学习算法和可用的选项，请提供的javadocs 。-It is a 100 pure Java library that you can use to apply N-Gr
GRNN
- 泛化回归神经网络GRNN(generalized regression NN)应用实例，适合学习使用。-Generalized regression neural network GRNN (generalized regression NN) application examples for learning to use.
EE4208_FaceRecog_ZhaoJian_v1
- The article demonstrates face detection SSE optimized C++ library for color and gray scale data with skin detection, motion estimation for faster processing, small sized SVM and NN rough face prefiltering, PCA/LDA/ICA/any dimensionality reduction/pro
Classification-toolbox
- 通过降维处理，高维数据的分类一般可以转换为2维数据分类。此源码包含一个2维-2类数据分类工具箱。包括：ML，K-NN，SVM，LS，DB-Through the dimension reduction processing, high dimensional data classification commonly can convert to 2 d data classification. This source includes a 2 d-two kinds of data classi
ELM-NN
- 基于极限学习机(ELM)的岩性识别。该算法是一种新的单隐层前馈神经网络(sLFNs)学习算法，不但可以简化参数选择过程，而且可以提高网络的训练速度。-Based on the traditional support vector machine (sVM) training is slow, difficult issues such as parameter selection, extreme learning machine is proposed based on (ELM) of li
Pprtools317a
- 模式识别 MATLAB 的工具箱,比较实用,包括SVM,IICA,PCA,NN等等模式识别算法.，已通过测试。 -MATLAB toolbox of pattern recognition, more practical, including SVM, IICA, PCA, NN pattern recognition algorithms. Has been tested.
CM
- 里面包括了关于TCM的各种算法，有TCM-NC算法，TCM-NN算法，TCM-SVM算法。对研究TCM算法的同学来说，很有帮助哦！-Inside, including a variety of algorithms about TCM, TCM-NC algorithm, TCM-NN algorithm, TCM-SVM algorithm. For students who study TCM algorithm helpful!
MATLAB-NN
- MATLAB神经网络的各类程序，包含遗传算法，神经网络，神经模糊，SVM等程序，十分实用。-All kinds of MATLAB neural network program, including genetic algorithms, neural networks, neuro-fuzzy, SVM and other procedures, very useful.
Pattern_Recognition
- 自己在硕士期间用到的各种模式识别，机器学习，数据挖掘算法的matlab程序。C4_5,NN，SVM，adaboost,KNN等-During their Master used a variety of pattern recognition, machine learning, data mining algorithm matlab program. C4_5, NN, SVM, adaboost, KNN, etc.
activity-recognition-based-on-SVM
- 基于支持向量机的人类活动识别，以日常生活中的10个活动进行识别。-Support Vector Machine (SVM) was first proposed in 1995 by Cortes and Vapnik [15] for solving classification and regression problems. The solving strategy of SVM on the multiple classification problems is com
SVM--ICA-and-PCA-and-NN
- SVM,ICA,PCA,NN等等模式识别算法,很有参考-SVM, ICA and PCA and NN, and so on pattern recognition algorithm, is of great reference value
data-mining
- nn/svm数据挖掘1,nn/svm数据挖掘2,nn/svm数据挖掘3,nn/svm数据挖掘4,nn/svm数据挖掘.(nn/svm1,nn/svm2,nn/svm3,nn/svm4,nn/svm,nn/svm)
Recognition
- 将数量较少的故障样本分为训练集和测试集，实现故障的分类和识别(A small number of fault samples are divided into training set and test set to realize fault classification and recognition.)