递归神经网络英文课件-Chapter 2 Machine learning basics

wMSEtrain = 0 ⇒ w||X(train)w − y(train)||22 = 0 w = (X(train)t X(train))−1X(train)t y(train) where X(train) = [x(1train), . . . , x(Ntrain)] and y(train) = [y1(train), . . . , yN(train)].
i
Why not use classification errors #{f (x(itrain)) = yi(train)}?
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Xiaogang Wang
Machine Learning Basics
Optimization
The choice of the objective function should be good for optimization Take linear regression as an example
y ∗ = arg max P(y = k |x)
k
(Duda et al. Pattern Classification 2000) Xiaogang Wang
Machine Learning Basics
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Regression
Predict real-valued output f : RD → RM
Xiaogang Wang
Machine Learning Basics
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Generalization
We care more about the performance of the model on new, previously unseen examples
The training examples usually cannot cover all the possible input configurations, so the learner has to generalize from the training examples to new cases
1 Performancetest = M
M
Error(f (x(i test)), yi(test))
Байду номын сангаас
i =1
We hope that both test examples and training examples are drawn from p(x, y ) of interest, although it is unknown
Mean squared error (MSE) for linear regression
MSEtrain
=
1 N
||wt x(itrain) − yi(train)||22
i
Cross entropy (CE) for classification
CEtrain
=
1 N
log P(y = yi(train)|x(itrain))
GEf = p(x, y )Error(f (x), y )
x,y
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Xiaogang Wang
Machine Learning Basics
Generalization
However, in practice, p(x, y ) is unknow. We assess the generalization performance with a test set {x(i test), yi(test)}
Machine Learning Basics
Xiaogang Wang
Machine Learning Basics
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Machine Learning
Xiaogang Wang
Machine Learning Basics
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Classification
f (x) predicts the category that x belongs to f : RD → {1, . . . , K }
f (x) is decided by the decision boundary As an variant, f can also predict the probability distribution over classes given x, f (x) = P(y |x). The category is predicted as
Decision boundary, parameters of P(y |x), and w in linear regression
Optimize an objective function on the training set. It is a performance measure on the training set and could be different from that on the test set.
Generalization error: the expected error over ALL examples
To obtain theoretical guarantees about generalization of a machine learning algorithm, we assume all the samples are drawn from a distribution p(x, y ), and calculate generalization error (GE) of a prediction function f by taking expectation over p(x, y )
Example: linear regression
D
y = wt x = wd xd + w0
d =1
(Bengio et al. Deep Learning 2014)
Xiaogang Wang
Machine Learning Basics
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Training
Training: estimate the parameters of f from {(x(itrain), yi(train))}