即参数化算法和非参数化算法. 参数化机器学习算法 可以大大简化学习过程,也可以限制可以学到的东西,将函数简化为已知形式的算法称为参数化机器学习算法.算法包括两个步骤: 为函数选择一个form. 从训练数据中学习函数的系数. 线性回归和逻辑回归就属于参数化机器学习算法. 非参数机器学习算法 没有对映射函数的形式做出强烈假设的算法称为非参数机器学习算法.因为不做假设,非参数算法可以自由地从训练数据中学习任何functional form. 非参数方法通常更灵活,达到更好的精度,但需要更多的数据和训练…

1.What are “Parametric Statistics”? 统计中的参数指的是总体的一个方面,而不是统计中的一个方面,后者指的是样本的一个方面.例如,总体均值是一个参数,而样本均值是一个统计量.参数统计检验对总体参数和数据的分布进行假设.这些类型的测试包括学生的T测试和方差分析测试,假设数据来自正态分布. A parameter in statistics refers to an aspect of a population, as opposed to a statistic,…

  Basic theory (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks, )  regression, classification. (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, dee…

Machine Learning by Andrew Ng | Stanford University | Coursera https://www.coursera.org/learn/machine-learning Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has g…

About this Course Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly i…

From:  http://www.pamitc.org/cvpr15/program.php Official Program for CVPR 2015 Monday, June 8 8:30am-8:40am Ballrooms A,B,C Rooms 302,304,306 Opening Remarks from Conference Chairs The opening remarks will be made from Ballrooms A,B,C, but a live vid…

A Statistical View of Deep Learning (III): Memory and Kernels Memory, the ways in which we remember and recall past experiences and data to reason about future events, is a term used frequently in current literature. All models in machine learning co…

1.EXEALL.m function EXEALL(FilePath, FileName)%执行所有流程% FilePath: 文件夹所在路径% FileName: 文件夹名称 FullPath = [FilePath , FileName , '\' , FileName , '.txt']; allPixelPath = [FilePath , FileName , '\', FileName , '.allpixel.csv']; TestdataPath = [FilePath , F…

CVPR2015 Papers震撼来袭! CVPR 2015的文章可以下载了,如果链接无法下载,可以在Google上通过搜索paper名字下载(友情提示:可以使用filetype:pdf命令). Going Deeper With ConvolutionsChristian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke…

目录 C1 Introduction to Statistical Learning 1.1Statistical Learning介绍: 1.1.1 估计 \(f\) 的目的:prediction和/或inference. 1.1.2 估计 \(f\) 的方法:parametric 或 non-parametric 1.2 评估模型准确性 1.2.1 回归的评估 1.2.2 Bias-Variance的平衡 1.2.3 分类的情况 C2 Linear Regression 2.1 简单线性回归…