AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (clasSification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class clasSification problem.
标签： well-known algorithm AdaBoost Adaptive
上传时间： 2014-01-15
上传用户：qiaoyue
very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern clasSification - A Comparative Study document
标签： Decision-Based Probabilistic Gaussian Networks
上传时间： 2014-01-02
上传用户：saharawalker
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for clasSification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
标签： foundations The consists sections
上传时间： 2017-06-22
上传用户：lps11188
流分类算法中的一种，Scalable Packet clasSification 非常有参考价值。。
上传时间： 2013-12-19
上传用户：yyyyyyyyyy
The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram clasSification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
标签： L. A. classifiers implements
上传时间： 2017-08-02
上传用户：无聊来刷下
Capabilities of the latest version of MultiSpec include the following. Import data Display multispectral images Histogram Reformat Create new channels Cluster data Define classes via designating rectangular Determine the best spectral features Classify a designated area in the data file List clasSification results
标签： Capabilities MultiSpec following the
上传时间： 2013-12-02
上传用户：源码3
支持向量机(SVM)实现的分类算法源码[matlab] -Support Vector Machine (SVM), a clasSification algorithm source code [Matlab]
上传时间： 2016-04-25
上传用户：shiaijianjun
主要是实现调制识别，区分几种常用的数字调制信号，包括ASK,FSK,PSK,QAM。含有两个文件夹 其一为特征参数的仿真；其二为正确识别率的仿真。 文件夹key feature simulink中： 运行程序会得到各特征参数之间区分图 从图中可看到特征参数的有效性。 文件夹clasSification rate simulink中： 运行main.m文件 可以得到正确识别率
标签： qam
上传时间： 2016-05-02
上传用户：ylqylq
Libsvm is a simple, easy-to-use, and efficient software for SVM clasSification and regression. It solves C-SVM clasSification, nu-SVM clasSification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM clasSification.
标签： LibSVM
上传时间： 2019-06-09
上传用户：lyaiqing
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data clasSification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
标签： recognition Bi-density machines support pattern vector twin for
上传时间： 2019-06-09
上传用户：lyaiqing
