sycong的专栏

随着信息技术和互联网的发展，人们逐渐从信息匮乏的时代走入了信息过载（information overload）的时代 。在这个时代，无论是信息消费者还是信息生产者都遇到了很大的挑战：对于信息消费者，从大量信息中找到自己感兴趣的信息是一件非常困难的事情；对于信息生产者，让自己生产的信息脱颖而出，受到广大用户的关注，也是一件非常困难的事情。推荐系统就是解决这一矛盾的重要工具。推荐系统的任务就是联系用户和信息，一方面帮助用户发现对自己有价值的信息，另一方面让信息能够展现在对它感兴趣的用户面前，从而实现信息消费者和信息生产者的双赢。

The dataset is collected by Yonsei University. We deployed our mobility monitoring system, named LifeMap, to collect mobility data over two months in Seoul, Korea. LifeMap used learning scheme proposed in following paper. Please refer this paper when you use our dataset. * Yohan Chon, Elmurod Talipov, Hyojeong Shin, and Hojung Cha. 2011. Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys '11). ACM, New York, NY, USA, 82-95. Visit our homepage for more information (http://lifemap.yonsei.ac.kr).

Kaggle房价预测代码 score=12% import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt

2015 2016北邮移动通信原理期末考试题

北邮2015-2016专硕概率论与随机过程期末考试题及答案

随机过程第四版刘次华课后答案

北邮2015-2016年 概率论与随机过程试题

wavelets on graphs via spectral We propose a novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling using the graph analogue of the Fourier domain, namely the spectral decomposition of the discrete graph Laplacian L . Given a wavelet generating kernel g and a scale parameter t, we define the scaled wavelet operator T t g = g ( t L ) . The spectral graph wavelets are then formed by localizing this operator by applying it to an indicator function. Subject to an admissibility condition on g, this procedure defines an invertible transform. We explore the localization properties of the wavelets in the limit of fine scales. Additionally, we present a fast Chebyshev polynomial approximation algorithm for computing the transform that avoids the need for diagonalizing L . We highlight potential applications of the transform through examples of wavelets on graphs corresponding to a variety of different problem domains.