machine learning

CSE 575: Sta*s*cal Machine Learning

Jingrui He CIDSE, ASU

Instance-based Learning

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1-Nearest Neighbor

Four things make a memory based learner: 1. A distance metric

Euclidian (and many more) 2. How many nearby neighbors to look at? One 1. A weigh:ng func:on (op:onal)

Unused

2. How to t with the local points? Just predict the same output as the nearest neighbor.

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Consistency of 1-NN Consider an es*mator fn trained on n examples

e.g., 1-NN, regression, ... Es*mator is consistent if true error goes to zero as amount of

data increases e.g., for no noise data, consistent if:

Regression is not consistent! Representa*on bias

1-NN is consistent (under some mild neprint)

What about variance???

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1-NN overts?

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k-Nearest Neighbor Four things make a memory based learner: 1. A distance metric

Euclidian (and many more) 2. How many nearby neighbors to look at?

k 1. A weigh:ng func:on (op:onal)

Unused

2. How to t with the local points? Just predict the average output among the k nearest neighbors.

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k-Nearest Neighbor (here k=9)

K-nearest neighbor for funcFon Hng smooth away noise, but there are clear deciencies. What can we do about all the discon*nui*es that k-NN gives us?

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Curse of dimensionality for instance-based learning

Must store and retrieve all data! Most real work done during tes*ng For every test sample, must search through all dataset very slow! There are fast methods for dealing with large datasets, e.g., tree-based

methods, hashing methods,

Instance-based learning o^en poor with noisy or irrelevant features

Support Vector Machines

w.x = j w(j) x(j) 10

Linear classiers Which line is beber?

Data:

Example i:

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Pick the one with the largest margin!

w.x = j w(j) x(j)

w.x + b = 0

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Maximize the margin

w.x + b = 0

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But there are a many planes

w.x + b = 0

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w.x + b = 0

Review: Normal to a plane

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w.x + b = +1

w.x + b = -1

w.x + b = 0

margin 2

x- x+

Normalized margin Canonical hyperplanes

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Normalized margin Canonical hyperplanes

w.x + b = +1

w.x + b = -1

w.x + b = 0

margin 2

x- x+

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Margin maximiza*on using canonical hyperplanes

w.x + b = +1

w.x + b = -1

w.x + b = 0

margin 2

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Support vector machines (SVMs)

w.x + b = +1

w.x + b = -1

w.x + b = 0

margin 2

Solve eciently by quadra*c programming (QP) Well-studied solu*on algorithms

Hyperplane dened by support vectors

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What if the data is not linearly separable?

Use features of features of features of features.

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What if the data is s*ll not linearly separable?

Minimize w.w and number of training mistakes Tradeo two criteria?

Tradeo #(mistakes) and w.w 0/1 loss Slack penalty C Not QP anymore Also doesnt dis*nguish near misses

and really bad mistakes

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Slack variables Hinge loss

If margin 1, dont care If margin < 1, pay linear penalty

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Side note: Whats the dierence between SVMs and logis*c regression?

SVM: LogisFc regression:

Log loss:

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Constrained op*miza*on

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Lagrange mul*pliers Dual variables

Moving the constraint to objecFve funcFon Lagrangian:

Solve:

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Lagrange mul*pliers Dual variables

Solving:

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Dual SVM deriva*on (1) the linearly separable case

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Dual SVM deriva*on (2) the linearly separable case

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Dual SVM interpreta*on

w.x + b = 0

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Dual SVM formula*on the linearly separable case

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Dual SVM deriva*on the non-separable case

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Dual SVM formula*on the non-separable case

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Why did we learn about the dual SVM?

There are some quadra*c programming algorithms that can solve the dual faster than the primal

But, more importantly, the kernel trick!!! Another lible detour

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Reminder from last *me: What if the data is not linearly separable?

Use features of features of features of features.

Feature space can get really large really quickly!

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Higher order polynomials

number of input dimensions

numbe

r of m

onom

ial terms

d=2

d=4

d=3

m input features d degree of polynomial

grows fast! d = 6, m = 100 about 1.6 billion terms

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Dual formula*on only depends on dot-products, not on w!

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Dot-product of polynomials

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Finally: the kernel trick!

Never represent features explicitly Compute dot products in closed form

Constant-*me high-dimensional dot-products for many classes of features

Very interes*ng theory Reproducing Kernel Hilbert Spaces

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Polynomial kernels

All monomials of degree d in O(d) opera*ons:

How about all monomials of degree up to d? Solu*on 0:

Beber solu*on:

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Common kernels

Polynomials of degree d

Polynomials of degree up to d

Gaussian kernels

Sigmoid

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Overvng?

Huge feature space with kernels, what about overvng??? Maximizing margin leads to sparse set of support vectors

Some interes*ng theory says that SVMs search for simple hypothesis with large margin

O^en robust to overvng

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What about at classica*on *me For a new input x, if we need to represent (x), we are in trouble!

Recall classier: sign(w.(x)+b) Using kernels we are cool!

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SVMs with kernels Choose a set of features and kernel func*on Solve dual problem to obtain support vectors i

At classica*on *me, compute:

Classify as

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Whats the dierence between SVMs and Logis*c Regression?

SVMs Logistic Regression

Loss function Hinge loss Log-loss

High dimensional features with kernels

Yes! No

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Kernels in logis*c regression

Dene weights in terms of support vectors:

Derive simple gradient descent rule on i

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Whats the dierence between SVMs and Logis*c Regression? (Revisited)

SVMs Logistic Regression

Loss function Hinge loss Log-loss

High dimensional features with kernels

Yes! Yes!

Solution sparse Often yes! Almost always no!

Semantics of output

Margin Real probabilities