机器学习前导---IBM讲座笔记
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Machinelearning Application:
Recomendation
Compute Vision
Diriveless Car
Web Search
Speech Recagnition
Question Answering
Game Player
Smart Healthcare
Supervised learningand Unsupervised learning
eg:
classification,regression clustreing,association analysis
Machine learningtasks(problem types)
supervisedlearning=predict a target y from input x
(and seml-supervisedlearning)y represents a catrgory or “class”
=>classification
binary:y belongs to{-1,+1} or y belongs to (0,1)
multickass:y belongsto {1,m} or y belongs to {0,m-1}
y is a real-valuenumber
=>regression ybelongs to R or y belongs to R^m
Above is predictivemodels
Below isDesctrisptive models
Unsupervisedlearning :no explicit prediction target y
model the probablitydistribution of x
=>densityestimation
discover underlyingstructure in data
=>clustering
=>demensionalityreduction
=>(unsupervised )representation learning
trainging:we learn apredictive function f by optimizing it so that it predicts well onthe traing set
Use forprediction:we can then use in new inputs that were not part of thetraing set
关键词:generalize:泛化
the goal of learningis not to learn perfectly (memorizing) the traing set.What'simportant is the ability for the predictor to generide well on new
problem dimensions:
number of explits
input demensionditynumber if input features
characterizing eachexample
targetdimensionality ex:number of classes
under-fitting andoverfitting
关键词:fitting:拟合
performance ontraining set is not a good estimate of generalization
support vectormachine(SVM支持向量机)
LogisticRegression(逻辑斯蒂回归)
SVM:
A powerful methodfor classification
Bettergeneralization
key ideas
--use kernelfunctionto transform low dimensional training sample to higherdimensions
--use quadraticprogramming (QP二次规划)to find the best classifier
support vectors arethose dataprints that the margin pushes up against
the soft margin SVMis equivalent to applying a hinge loss
empirical loss +regularization 损失函数与正则化
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