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feature_selection
- 自己编的特征选择程序，分别包括用顺序前进法(SFS),顺序后退法(SBS),增l 减r 法(l–r)、SFFS法进行选择的程序-own addendum to the feature selection procedures, including the use of sequential forward (SFS). back order (SBS), by reducing r l (l-r), SFFS method to choose the procedure
Srinivas_Mukkamala
- 两种SVM特征选择方法，用PPT详细的阐述了作者的思路和实验情况，非常清除明白。-two feature selection method, the PPT detailed descr iption of the author's ideas and experiments, understand very clear.
New_Kdd1
- 特征选择算法的java 实现,并且使用Jfreechart实现图形显示 -feature selection algorithm to achieve the java, and the use of graphics to achieve Jfreechart
Kdd
- 特征选择算法的改进．．．比较实践证明是个优秀的算法-feature selection algorithm improvements. . . Practice has proved that it is more an outstanding Algorithm
cprogramtogetthecharactervalueofavector
- c语言矩阵特征值求解代码，在求解特征选择和特征提取时候使用的。欢迎各位测试，提意见。-c matrix eigenvalue language code for the solution of feature selection and feature extraction often used. Welcome to test opinions.
jiansuo
- 这是我本科的毕业设计做的关于医学图像的基于形状的图像检索。预处理用小波去噪 ，特征选用不变矩。-This is my undergraduate graduation design done on the medical images based on the shape of the image retrieval. Pretreatment with wavelet denoising, feature selection invariant moment.
Using_Linux_to_Implement_8-and16-BitDevice_Network
- Abstract: By using gateway systems on large 32-bit platforms, networks of small, 8- and 16-bit microcontrollers can be monitored and controlled over the Internet. With embedded Linux, these gateways are easily moved from full-blown host PCs to
FCBF-java
- Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce dimensionality, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlat
Boosting-beta2
- AdaBoost is an efficient tool in machine learning. It can combine a series of weak learners into a strong learner. Besides pattern classification, it also can be applied into feature selection. This document explains the use of AdaBoost.
ICA2000_reprint.doc
- 具有带通选择性的ICA算法可以改善对于带通时间序列的分离以及对于周期性脑功能响应信号的提取. 因此本文提出的方案可将被估计信号, 如:周期性响应信号以及具有平滑空间分布的脑功能激活区, 的先验特性以特征选择的方式加入ICA算法用以提高对此类信号的估计-with selective ICA algorithm can be improved for the band pass time series, as well as for the separation of brain function
YOLi_ICASSP05
- 本文提出一种用于独立成份分析(ICA)的特征选择滤波方案用于改善ICA算法对关键独立成份(SOI)的分离和提取,关键独立成份在其信号样本数据的空间分布上具有一定特征. 本文以平滑滤波为例,表明加入此类特征滤波的ICA算法可以改善对于视觉功能区等平滑图象信号的提取. 因此, 这种特征滤波技术在估计具有平滑特性的脑功能成像方面具有潜在的应用价值.-for Independent component analysis (ICA) feature selection filtering program
GASVM.用遗传算法进行特征选取和svm参数优化的程序
- 用遗传算法进行特征选取和svm参数优化的程序。遗传算法工具箱goat已在压缩包 需要安装libsvm就可以直接运行。数据集采用UCI中的german数据集，并完成归一化操作,Genetic algorithm with feature selection and parameter optimization svm procedures. Genetic Algorithm Toolbox in goat need to install libsvm package can be run dir
Object-Recognition-via-Sparse-PCA 利用稀疏主分量分析实现目标识别中的特征提取
- 利用稀疏主分量分析实现目标识别中的特征提取，包括论文和仿真代码。-Informative Feature Selection for Object Recognition via Sparse PCA
ComputationalMethodsofFeatureS
- 一本关于模式识别中的特征选择的计算方法和模型的文集，比较实用。,A book on the various feature selection methods and models in pattern recognition. Very useful.
Feature_selector_Matlab
- feature subset selection methods
Binary Genetic Algorithm Feature Selection (2)
- Binary GA selection method
feature selection
- 各种信号特征提取，最大值，最小值，均值，商(Signal feature extraction)
2
- This paper presents an online feature selection algorithm for video object tracking. Using the object and background pixels from the previous frame as training samples, we model the feature selection problem as finding a good subset of features to
OptTest
- feature selection optimization