粘贴两个典型的例子,只是基础内容,帮助理解. (1) Solution: (2) Solution:…

Relevant Readable Links Name Interesting topic Comment Edwin Chen 非参贝叶斯   徐亦达老板 Dirichlet Process 学习目标:Dirichlet Process, HDP, HDP-HMM, IBP, CRM Alex Kendall Geometry and Uncertainty in Deep Learning for Computer Vision 语义分割 colah's blog Feature Visu…

以下笔记参考自Boyd老师的教材[Convex Optimization]. I. Mathematical Optimization 1.1 定义 数学优化问题(Mathematical Optimization) 有如下定义: \[ \begin{align} &minimize \, f_0(x) \notag \\ &subject \, to \, f_i(x)≤b_i, \, i=1,...,m \tag{1.1} \end{align} \] 向量\(x=(x_1,...,x…

Support Vector Machines for classification To whet your appetite for support vector machines, here’s a quote from machine learning researcher Andrew Ng: “SVMs are among the best (and many believe are indeed the best) ‘off-the-shelf’ supervised learni…

转载自:增广拉格朗日乘子法(Augmented Lagrange Method) 增广拉格朗日乘子法的作用是用来解决等式约束下的优化问题, 假定需要求解的问题如下: minimize f(X) s.t.: h(X)=0 其中,f:Rn->R; h:Rn->Rm 朴素拉格朗日乘子法的解决方案是: L(X,λ)=f(X)+μh(X); μ:Rm 此时,求解L对X和μ的偏导同时为零就可以得到最优解了. 增广拉格朗日乘子法的解决方案是: Lc(x,λ)=f(X)+μh(X)+1/2c|h(X)|2 每…

一.SciPy库概述 1.numpy提供向量和矩阵的相关操作,高级计算器 2.SciPy在统计.优化.插值.数值积分.视频转换等,涵盖基础科学计算相关问题. (额,对统计和概率,数理完全一窍不通) 3.量化分析中,运用最广泛的是统计和优化的相关技术,为本章重点. 4.涉及矩阵代数,可以暂时跳过(正有此意,这个以后再进行系统学习,对于后面的涉及,再仔细推敲,先解决业务问题,再学习后面的基础知识). 5.相关模块: import numpy as np import scipy.stats as s…

1. Increasing resources on the critical path activities may not always shorten the length of the project with the following reasons or examples .which may bring the LEAST influence to resource increase ? A certain activities are time dependent rather…

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro…

Basis(基础): SSE(Sum of Squared Error, 平方误差和) SAE(Sum of Absolute Error, 绝对误差和) SRE(Sum of Relative Error, 相对误差和) MSE(Mean Squared Error, 均方误差) RMSE(Root Mean Squared Error, 均方根误差) RRSE(Root Relative Squared Error, 相对平方根误差) MAE(Mean Absolute Error, 平均绝…

差分进化算法 (Differential Evolution)   Differential Evolution(DE)是由Storn等人于1995年提出的,和其它演化算法一样,DE是一种模拟生物进化的随机模型,通过反复迭代,使得那些适应环境的个体被保存了下来.但相比于进化算法,DE保留了基于种群的全局搜索策略,采用实数编码.基于差分的简单变异操作和一对一的竞争生存策略,降低了遗传操作的复杂性.同时,DE特有的记忆能力使其可以动态跟踪当前的搜索情况,以调整其搜索策略,具有较强的全局收敛能力和鲁棒…