Machine Learning知识要点
笛卡尔乘积定义 笛卡尔乘积是指在数学中，两个集合X和Y的笛卡尓积（Cartesian product），又称直积，表示为X×Y，第一个对象是X的成员而第二个对象是Y的所有可能有序对的其中一个成员 。
HMM , factor graph 和 MRF是一个有向图一个无向图。
sum product algorithm 是belief propogration. 看Bishop的第八章
LSI, PLSI, TransE,TransR Trans 系列：http://www.sohu.com/a/116866488_465975
PCA
LDA
LSA
matrix factorization
EM
SVM
Random forest
其他：
KMP算法
Hash表算法
genetic algorithm
https://segmentfault.com/a/1190000004155021
http://blog.sina.com.cn/s/blog_4cde15140100gi2u.html
http://blog.csdn.net/v_JULY_v/article/details/6132775
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://www.researchgate.net/profile/Clara_Pizzuti/publication/220739952_Community_detection_in_social_networks_with_genetic_algorithms/links/542ea6a50cf277d58e8ed068.pdf
https://arxiv.org/pdf/cond-mat/0604419.pdf
http://see.xidian.edu.cn/iiip/mggong/down/PHYSA2012Gong.pdf
http://arxiv.org/pdf/cond-mat/0501368.pdf
https://pdfs.semanticscholar.org/c342/c914dff35d6630c5963a54e3a07adf84f44f.pdf
…
http://arxiv.org/pdf/cond-mat/0308217.pdf
network modularity
During initial population creation, each node is assigned a random community identifier. However we need a mechanism to give a bias for initial placement of nodes into communities. If two nodes are to be in the same community, they should have connectivity with each other; in the simplest case they might be neighbors. From this assumption, after assigning random community IDs to nodes, we randomly select some nodes and assign their community IDs to all of their neighbors. This bias in the initial population creation improves the convergence of the algorithm and eliminates unnecessary iterations.