机器学习-黄迪2 线性回归

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机器学习

Machine Learning

北京航空航天大学计算机学院

School of Computer Science and Engineering, Beihang University

黄迪刘庆杰

2018年秋季学期

Fall 2018

部分内容来源于C. Bishop和A. NG等人的课程以及互联网资源

第一章：机器学习概述(续)

Chapter 1: Introduction to Machine Learning

训练和测试

训练集

(观测)

通用数据集

(非观测)

测试集

(非观测)

数据获取实际运用

Training is the process of making the system able to learn.

No free lunch rule:

–Training set and testing set come from the same distribution

–Need to make some assumptions or bias

训练和测试

There are several factors affecting the performance:

–Types of training provided

–The form and extent of any initial background knowledge

–The type of feedback provided

–The learning algorithms used

Two important factors:

–Modeling

–Optimization

评价标准

Supervised: Low E-out or maximize probabilistic terms

Unsupervised: Minimum quantization error, Minimum

distance, MAP, MLE (Maximum Likelihood Estimation)

机器学习目标

E-in: for training set

E-out: for testing set

Under-fitting VS. Over-fitting (fixed N)

机器学习目标

error

(model = hypothesis + loss

functions)

机器学习目标

Components of generalization error

Bias: how much the average model over all training

sets differ from the true model?

–Error due to inaccurate assumptions/simplifications made

by the model

Variance: how many models estimated from different

training sets differ from each other

机器学习目标

Under-fitting: model is too “simple” to represent all the

relevant class characteristics

–High bias and low variance

–High training error and high test error

Over-fitting: model is too “complex” and fits irrelevant

characteristics (noise) in the data

–Low bias and high variance

–Low training error and high test error

机器学习目标

Models with too few parameters

are inaccurate because of a large

bias (not enough flexibility).

Models with too many parameters

are inaccurate because of a large

variance (too much sensitivity to

the sample).

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机器学习MachineLearning北京航空航天大学计算机学院SchoolofComputerScienceandEngineering,BeihangUniversity黄迪刘庆杰2018年秋季学期Fall2018部分内容来源于C.Bishop和A.NG等人的课程以及互联网资源第一章：机器学习概述(续)Chapter1:IntroductiontoMachineLearning训练和测试训练集(观测)通用数据集(非观测)测试集(非观测)数据获取实际运用Trainingistheprocessofmakingthesystemabletolearn.Nofreelunchrule:–Tr...

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