2.1. Binary Variables 1. Bernoulli distribution, p(x = 1|µ) = µ 2.Binomial distribution + 3.beta distribution(Conjugate Prior of Bernoulli distribution) The parameters a and b are often called hyperparameters because they control the distribution of
This is the second post in Boosting algorithm. In the previous post, we go through the earliest Boosting algorithm - AdaBoost, which is actually an approximation of exponential loss via additive stage-forward modelling. What if we want to choose othe
expectation Description 给出一棵带权值的树,我们假设从某个节点出发,到目标节点的时间为两个节点之间的最短路.由于出发节点不好选取,所以选在每个节点都有一定的概率,现在我们要求从出发点到目标节点的期望时间(即每个节点到目标点的时间*概率). 为了避免精度错误, 直接给出了每个节点所占的权值, 那么每个节点的概率就是节点权值/总权值和( 注意查看实际输出要求). Input 输入第一行为一个正整数 n 表示树的节点数目, 节点编号从 1 到 n. 接下来一行 n 个整数 vi
Conditional project or library reference in Visual Studio In case you were wondering why you haven’t heard from me in a while, I’ve been busy, which isn’t really of much importance unless you know me on a personal level. What is relevant is that I re
namespace RefactoringLib.Ploymorphism.Before { public class Customer { } public class Employee : Customer { } public class NonEmployee : Customer { } public class OrderProcessor { public decimal ProcessOrder(Customer customer, IEnumerable<Product> p
Abstract Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch be
Daniil's blog Machine Learning and Computer Vision artisan. About/ Blog/ Image Segmentation with Tensorflow using CNNs and Conditional Random Fields Tensorflow and TF-Slim | Dec 18, 2016 A post showing how to perform Image Segmentation with a recentl
