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icaML
- This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixin
GA__MATLAB
- 探讨了在 Mh T I AB环境中实现遗传算法仿真 的方法 ， 并 以一个 简单的求函数最值的问 题作为遗传算法的应用实铡， 说明遗传算法的全局寻优性及用 M AI I AB实现仿真的可行性。-A me f l ~dt o r e Aa z e g e me f i e t I 皿 i n MKI I AB i s d ~- u s s e d．A ha e t i o ~o p t h r f i z a f i o n p r o b l e m i s p r e
Procdump
- dump 系统进程信息，结合mimikatz 使用，可以获取系统hash-Using ProcDump usage: procdump [-a] [[-c|-cl CPU usage] [-u] [-s seconds]] [-n exceeds] [-e [1 [-b]] [-f <filter,...>] [-g] [-h] [-l] [-m|-ml commit usage] [-ma |-mp] [-o] [-p|-pl counter threshold] [-r
exam_LED
- LED灯闪烁 - XF.pjt - Debug - [XF.c] C:\CCStudio_v3.3\C5500\cgtools\bin\cl55 -g -q -fr E:/Debug/Easy5509/EX01_XF/Debug -d _DEBUG -ml -@ Debug.lkf XF.c [Linking...] C:\CCStudio_v3.3\C5500\cgtools\bin\cl55 -@ Debug.lkf <Linking> >&
MLkNN
- ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbo
TELE - 653 Digital Coding
- e Handbook Entry Communication concepts: Fourier transforms, random signals, Transmitter and receiver filters, matched filter, Nyquist criterion. Digital Modulation schemes: M-ary ASK, QPSK, FSK, CPM, spectral analysis of modulated signals, ML and
sfa_adofiles
- 用于创建的模型- sfmodel和sfsystem供应的初始值—。用于（1）强调利用电子（样品）的ML估计在expte。（2）如果是测试工作（3）给边缘（后缀），这样用户可以选择创建，比如，z1_mypostfix。(To be used to supply initial values for models created by -sfmodel- and -sfsystem-. To do: (1) emphasize the use of e(sample) in ExpTE follo