我們應(yīng)該好哈乘此資源，不是積分隨便亂發(fā)額。哈哈哈哈，seishem 就是囊奧‘ 啊’
上傳時(shí)間： 2016-08-20
上傳用戶：ysystc699
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上傳時(shí)間： 2016-08-20
上傳用戶：ysystc699
請您認(rèn)真填寫上傳信息，管理員將會對您提交的內(nèi)容進(jìn)行審核。若用戶上傳不合格資源，則會清空該用戶的所有下載積分，以及限制該賬戶上傳。
上傳時(shí)間： 2016-08-20
上傳用戶：ysystc699
此方法是對lmd方法的創(chuàng)新，有效的解決了lmd的斷電效應(yīng)
上傳時(shí)間： 2016-08-25
上傳用戶：gaoqinwu
模式識別matlab工具箱,包括SVM,ICA,PCA,NN等等模式識別算法,很有參考價(jià)值
上傳時(shí)間： 2016-12-25
上傳用戶：wwwnada
The AP2406 is a 1.5Mhz constant frequency, slope compensated current mode PWM step-down converter. The device integrates a main switch and a synchronous rectifier for high efficiency without an external Schottky diode. It is ideal for powering portable equipment that runs from a single cell lithium-Ion (Li+) battery. The AP2406 can supply 600mA of load current from a 2.5V to 5.5V input voltage. The output voltage can be regulated as low as 0.6V. The AP2406 can also run at 100% duty cycle for low dropout operation, extending battery life in portable system. Idle mode operation at light loads provides very low output ripple voltage for noise sensitive applications. The AP2406 is offered in a low profile (1mm) 5-pin, thin SOT package, and is available in an adjustable version and fixed output voltage of 1.2V, 1.5V and 1.8V
上傳時(shí)間： 2017-02-23
上傳用戶：w124141
由STC89C51單片機(jī)來控制DHT11傳感器采集的溫濕度的轉(zhuǎn)換、1602液晶屏的顯示，以及蜂鳴器的報(bào)警。
上傳時(shí)間： 2018-04-27
上傳用戶：luson
SVM--支撐向量機(jī)的相關(guān)源代碼 數(shù)據(jù) 與案例分析
標(biāo)簽： MATLAB 神經(jīng)網(wǎng)絡(luò) 案例分析
上傳時(shí)間： 2018-11-26
上傳用戶：marsalon
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
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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