有向無(wú)環(huán)圖支持向量(DAG-SVMS)多類分類方法,是一種新的多類分類方法。該方法采用了最小超球體類包含作為層次分類依據(jù)。試驗(yàn)結(jié)果表明,采用該方法進(jìn)行多類分類,跟已有的分類方法相比有更高的分類精度。
上傳時(shí)間： 2016-03-19
上傳用戶：1109003457
This is SvmFu, a package for training and testing support vector machines (SVMs). It s written in C++. It uses templates. The advantage of templates is that the types of kernel values and data points can be varied to suit the problem.
標(biāo)簽： machines training package testing
上傳時(shí)間： 2015-07-03
上傳用戶：zhengzg
多分類支持向量機(jī)實(shí)現(xiàn)方法的分析比較：一對(duì)一、一對(duì)多和DAG的對(duì)比，比較專業(yè)，
標(biāo)簽： DAG 分類 分析比較 支持向量機(jī)
上傳時(shí)間： 2015-08-13
上傳用戶：hoperingcong
用C語(yǔ)言實(shí)現(xiàn)了編譯系統(tǒng)中的DAG算法。 處理的比較簡(jiǎn)單。
標(biāo)簽： DAG C語(yǔ)言 編譯系統(tǒng) 算法
上傳時(shí)間： 2014-01-09
上傳用戶：asddsd
Dag Erling http library source code
標(biāo)簽： library Erling source http
上傳時(shí)間： 2013-12-05
上傳用戶：ecooo
New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. It also lets you direcly optimize multivariate performance measures like F1-Score, ROC-Area, and the Precision/Recall Break-Even Point.
標(biāo)簽： classification algorithm for training
上傳時(shí)間： 2014-12-20
上傳用戶：stvnash
MSJ-06Ⅱ-A 美國(guó)UNION UNION FLAG 型式番號(hào)：SVMS-01 歐洲聯(lián)盟AEU AEU ENACT 型式番號(hào)：AEU-09 AEU ENACT（量產(chǎn)型） 型式編號(hào)：AEU-09
標(biāo)簽： AEU ENACT UNION FLAG
上傳時(shí)間： 2014-01-06
上傳用戶：nanxia
This example demonstrates how to use WEKA s SVMs classifier in Matlab.
標(biāo)簽： demonstrates classifier example Matlab
上傳時(shí)間： 2013-12-18
上傳用戶：dbs012280
這個(gè)課程項(xiàng)目完成了給定DAG graph，找到所有拓?fù)渑判虿⑶逸敵觥Ｓ玫搅酥羔樅玩湵怼?duì)于學(xué)習(xí)C/C++和數(shù)據(jù)結(jié)構(gòu)比較有幫助。
上傳時(shí)間： 2017-06-14
上傳用戶：2467478207
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
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