原创 《Mining Cross-Cultural Differences and Similarities in Social Media 》筆記
此篇博客是關於一篇發表在ACL2018上的論文《Mining Cross-Cultural Differences and Similarities in Social Media》的閱讀筆記。該博客主要用於敘述個人對於所讀論文的理解,如
原创 UVA12034Race
//UVA12034Race #include<cstdio> #include<cstring> const int maxn = 1000; const int M = 10056; int f[maxn + 5], c[maxn +
原创 UVA1639Candy
//UVA1639Candy #include<cstdio> #include<cstring> #include<cmath> const int maxn = 200000; long double F[maxn]; double
原创 UVA12230CrossingRivers
//UVA12230CrossingRivers #include<cstdio> #include<cstring> const int maxn = 10 + 5; double L[maxn], v[maxn]; int main(
原创 UVA10820SendATable
//UVA10820SendATable #include<cstdio> #include<cstring> #include<cmath> const int maxn = 50000; int phi[maxn + 5]; long
原创 《Parameter estimation for text analysis》閱讀筆記(五)
本文內容爲Parameter estimation for text analysis閱讀筆記第五篇,如有錯誤或疏漏之處,懇請各位批評指正! 簡介: Latent Dirichlet Allocation(LDA)是一種概率生成模型,它通
原创 UVA10288Coupons
//UVA10288Coupons #include<cstdio> #include<cstring> #include<sstream> #include<iostream> using namespace std; typedef
原创 UVA580CriticalMass
///UVA580CriticalMass #include<cstdio> #include<cstring> const int maxn = 30; int f[maxn + 5], g[maxn + 5]; int main()
原创 UVA10900SoYouWantToBeA2^n-aire?
//UVA10900SoYouWantToBeA2^n-aire? #include<cstdio> #include<cstring> const int maxn = 20 + 5; double d[maxn]; int main
原创 UVA11346Probability
//UVA11346Probability #include<cstdio> #include<cstring> #include<cmath> int main() { int N; double a, b, s; scan
原创 UVA11971Polygon
//UVA11971Polygon #include<cstdio> #include<cstring> typedef long long LL; LL Gcd(LL a, LL b) { return b == 0 ? a :
原创 UVA1637DoublePatience
//UVA1637DoublePatience #include<cstdio> #include<cstring> #include<vector> #include<map> using namespace std; char inp
原创 UVA1638PoleArrangement
//UVA1638PoleArrangement #include<cstdio> #include<cstring> int n, l, r; const int maxn = 20; long long d[maxn + 5][max
原创 《Parameter estimation for text analysis》閱讀筆記(四)
本文內容爲Parameter estimation for text analysis閱讀筆記第四篇,如有錯誤或疏漏之處,懇請各位批評指正! 簡介: 本文將主要介紹兩類關聯性較大的方法,用以描述一個系統的概率行爲:貝葉斯網絡 (Bayes
原创 《Parameter estimation for text analysis》閱讀筆記(三)
本文內容爲Parameter estimation for text analysis閱讀筆記第三篇,如有錯誤或疏漏之處,懇請各位批評指正! 簡介: 雖然貝葉斯模型的計算往往非常棘手(evidence往往難以求得),但由於我們可以自由選擇