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CLASSification

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yo

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.

標(biāo)簽： well-known algorithm AdaBoost Adaptive

上傳時(shí)間： 2014-01-15

上傳用戶：qiaoyue
very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Class

very good Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern CLASSification - A Comparative Study document

標(biāo)簽： Decision-Based Probabilistic Gaussian Networks

上傳時(shí)間： 2014-01-02

上傳用戶：saharawalker
The book consists of three sections. The first, foundations, provides a tutorial overview of the pri

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.

標(biāo)簽： foundations The consists sections

上傳時(shí)間： 2017-06-22

上傳用戶：lps11188
流分類算法中的一種

流分類算法中的一種，Scalable Packet CLASSification 非常有參考價(jià)值。。

標(biāo)簽： 流分類算法

上傳時(shí)間： 2013-12-19

上傳用戶：yyyyyyyyyy
The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. L

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 "

標(biāo)簽： L. A. classifiers implements

上傳時(shí)間： 2017-08-02

上傳用戶：無聊來刷下
Capabilities of the latest version of MultiSpec include the following. Import data Dis

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

標(biāo)簽： Capabilities MultiSpec following the

上傳時(shí)間： 2013-12-02

上傳用戶：源碼3
SVM(matlab)多分類

支持向量機(jī)(SVM)實(shí)現(xiàn)的分類算法源碼[matlab] -Support Vector Machine (SVM), a CLASSification algorithm source code [Matlab]

標(biāo)簽： matlab SVM 分類

上傳時(shí)間： 2016-04-25

上傳用戶：shiaijianjun
16qam

主要是實(shí)現(xiàn)調(diào)制識(shí)別，區(qū)分幾種常用的數(shù)字調(diào)制信號(hào)，包括ASK,FSK,PSK,QAM。含有兩個(gè)文件夾其一為特征參數(shù)的仿真；其二為正確識(shí)別率的仿真。文件夾key feature simulink中：運(yùn)行程序會(huì)得到各特征參數(shù)之間區(qū)分圖從圖中可看到特征參數(shù)的有效性。文件夾CLASSification rate simulink中：運(yùn)行main.m文件可以得到正確識(shí)別率

標(biāo)簽： qam

上傳時(shí)間： 2016-05-02

上傳用戶：ylqylq
LibSVM

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.

標(biāo)簽： LibSVM

上傳時(shí)間： 2019-06-09

上傳用戶：lyaiqing
Bi-density twin support vector machines

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

標(biāo)簽： recognition Bi-density machines support pattern vector twin for

上傳時(shí)間： 2019-06-09

上傳用戶：lyaiqing