econ

Econ 3818Spring 2019R data project Unlike your other R assignments, this assignment is individual work only. You may discuss your project with classmates but you must have your own unique project.. Final write-up is due via email by 5 pm on Monday, A

Project ECON 427, 1. Predicting Stock Price MovementsThe goal of this project is to predict stock pricesby applying machine learning techniquesto data from StockTwits, a social media platform for investors. We extractfeatures from textual data, and f

ECON 331 - Homework #1due in class October 2nd at 2.30pmLate homework will not be considered. Show detailed calculations and/or provide detailedexplanations to get full credit. Partial credit may be given.• Exercise 1. Show by recurrence that the fol

CoffeeScript is a little language that compiles into JavaScript. Underneath that awkward Java-esque patina, JavaScript has always had a gorgeous heart. CoffeeScript is an attempt to expose the good parts of JavaScript in a simple way. The golden rule

MATLAB基础知识 l  Imread:  读取图片信息: l  axis:轴缩放:axis([xmin xmax ymin ymax zmin zmax cmin cmax]) 设置 x.y 和 z 轴范围以及颜色缩放范围(请参阅 caxis).v = axis 返回包含 x.y 和 z 轴缩放因子的行矢量.v 具有 4 或 6 个分量,具体分别取决于当前坐标轴是二维还是三维.返回值是当前坐标轴的 XLim.Ylim 和 ZLim 属性.   基于 x.y 和 z 数据的最小值和最大值,ax

#!/bin/bash [ $1 = 'http_server' ] && WEBGROUP=$1,FRONTEND || WEBGROUP=$1,BACKEND case "$2" in "comp" ) IN=`/bin/echo "show stat"|/usr/sbin/socat /var/run/haproxy.sock stdio|awk -v n=$WEBGROUP -F, '$0~n{print $52}'` O

PCA的一些基本资料 最近因为最人脸表情识别,提取的gabor特征太多了,所以需要用PCA进行对提取的特征进行降维. 本来最早的时候我没有打算对提取的gabor特征进行降维,但是如果一个图像时64*64,那么使用五个尺度八个方向的gabor滤波器进行滤波,这样提取的特征足足有64*64*5*8这么多,如果图像稍微大一点,比如128*128的图像,那么直接提取的特征就会几十万,所以不降维的话直接用SVM训练分类器是非常困难的. 所以在这段时间我就学习了一下PCA降维的基本原理和使用方法,网上给出的

ISSN Abbreviated Journal Title Full Title Category Subcategory Country total Cites IF        2013-2014 IF 2012-2013 IF 2011-2012 IF 2010-2011 IF 2009-2010 IF 2008-2009 IF 2007-2008 5-Year Impact Factor Immediacy Index Articles Cited Half-Life Eigenfa

接上文:<压缩感知中常用的待还原信号种类>,http://blog.csdn.net/zhyoulun/article/details/25600311 在压缩感知中,观测矩阵就是指y=Ax中的A.A是一个n*m的矩阵,矩阵中的每一个元素独立同分布于一个特定的分布.分布的种类如下: 1.USE.一致球集合,Uniform spherical ensemble,首先计算出一个n*m的矩阵,矩阵中的每一个元素服从标准正态分布,然后对这个矩阵的每一列做归一化. 2.RSE.随机信号集合,Random