搜索资源列表
supp
- Providing a unique perspective on the state of the art in SVMs, with a particular focus on classification, this thoroughly updated new edition includes a more rigorous performance comparison of classifiers and regressors
Ensemble-learning-based-on-GMDH
- 基于自组织数据挖掘的多分类器集成选择的程序-Multiple classifiers ensemble selection based on GMDH
l7-filter-userspace-0.10.tar
- L7-filter is a classifier for Linux s Netfilter that identifies packets based on application layer data. It can classify packets as Kazaa, HTTP, Jabber, Citrix, Bittorrent, FTP, Gnucleus, eDonkey2000, etc., regardless of
testKNN
- KNN 分类器的VC实现。。。可以直接运行。-KNN classifiers realize the VC. . . Can be directly run.
How-to-build-classifier-
- 如何利用opencv训练自己的分类器，内有多篇资料，本人用过一次，可能样本太少，效果不太好-How-to build a cascade of boosted classifiers based on Haar-like features.
Algorithms-of-the-intelligent-web
- 智能web算法英文原版, 描述利用各种技术对web应用进行智能化的处理； 涵盖五类重要算法：搜索、推荐、聚类、分类和分类器融合-This is the English original version of <Algorithms of the Intelligent Web>. It is about the use of techniques that enable the intelligent p
11
- GENETIC CLASSIFIERS JUST THIS FILE CAN HELP U!
dct_nnc
- 结合DCT和NNC进行人脸识别。先利用DCT提取特征，然后利用最近邻分类器分类，在ORL人脸库上测试效果不错。-The combination of DCT and NNC for face recognition. First DCT Feature Extraction, and then use the nearest neighbor classifiers, good test results on the ORL face
Matlab_SVM
- SVM实现数据分类，可自行选取核函数，用于图像分割操作。程序可自动训练分类平面。-SVM for data classification, are free to select the kernel function，For image segmentation operation. The program can automatically train classifiers plane.
adaboost-coding
- adaboost 源码，综合多个弱分类器，通过动态修改元组权值以及得到的分类器权值，提升为复合强分类器。-AdaBoost source code, several weak classifiers, by dynamically modifying the tuple classifier obtained by weight and weight, enhance the strong classifier for composit
stprtool
- pattern recognition classifiers in Matlab code
adaboost_version1e
- 这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” -This a classic AdaBoost implementation, in on
Software-faults-prediction-using-multiple-classif
- Abstract—In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This pap
OpenCVA
- 介绍了几种分类器的原理，并使用opencv实现了其算法-Introduced the principle of several classifiers, and use of opencv its algorithm
ANN-BP
- 对“data2.m”数据，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-The "data2.m" data, which half of the data using the ANN-BP algorithm design classifiers, the other half of the data used to test the classifier performance.
TriTrain
- 周志华提出的Tri-Training源代码，利用三个分类器的半监督学习方法，Java版本-Zhou Zhihua proposed by the Tri-Training source code, use semi-supervised learning method, the Java version of the three classifiers
BP
- 对“data2.m”数据，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。-The " data2.m" data, which half of the data using the ANN-BP algorithm design classifiers, the other half of the data used to test the classifier performance.
ensemble-toolbox
- a very useful toolbox for classifiers combination: majority voting, max, min, average mesures and others
AdaBoost
- 这是一本介绍AdaBoost算法的资料。Adaboost是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器-This is an introduction AdaBoost algorithm information. Adaboost is an iterative algorithm, the core idea for a training set of
multi-class-problem
- 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class p