Classification II Tamara Berg CS 560 Artificial Intelligence Many slides throughout the course...

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Classification II

Tamara Berg

CS 560 Artificial Intelligence

Many slides throughout the course adapted from Svetlana Lazebnik, Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer, Rob Pless, Killian Weinberger, Deva Ramanan

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Announcements

• HW3 due tomorrow, 11:59pm

• Midterm2 next Wednesday, Nov 4 – Bring a simple calculator– You may bring one 3x5 notecard of notes (both sides)

• Monday, Nov 2 we will have in class practice questions

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Midterm Topic List

Probability– Random variables – Axioms of probability– Joint, marginal, conditional probability distributions – Independence and conditional independence– Product rule, chain rule, Bayes rule

Bayesian Networks General– Structure and parameters – Calculating joint and conditional probabilities– Independence in Bayes Nets (Bayes Ball)

Bayesian Inference– Exact Inference (Inference by Enumeration, Variable Elimination)– Approximate Inference (Forward Sampling, Rejection Sampling, Likelihood

Weighting)– Networks for which efficient inference is possible

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Midterm Topic ListNaïve Bayes

– Parameter learning including Laplace smoothing– Likelihood, prior, posterior – Maximum likelihood (ML), maximum a posteriori (MAP) inference – Application to spam/ham classification and image classification

HMMs– Markov Property– Markov Chains– Hidden Markov Model (initial distribution, transitions, emissions)– Filtering (forward algorithm)– Application to speech recognition and robot localization

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Midterm Topic List

Machine Learning– Unsupervised/supervised/semi-supervised learning– K Means clustering– Hierarchical clustering (agglomerative, divisive)– Training, tuning, testing, generalization– Nearest Neighbor– Decision Trees– Boosting– Application of algorithms to research problems (e.g. visual word discovery, pose

estimation, im2gps, scene completion, face detection)

The basic classification framework

y = f(x)

• Learning: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the parameters of the prediction function f

• Inference: apply f to a never before seen test example x and output the predicted value y = f(x)

output classification function

input

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Classification by Nearest Neighbor

Word vector document classification – here the vector space is illustrated as having 2 dimensions. How many dimensions would the data actually live in?

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Classification by Nearest Neighbor

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Classification by Nearest Neighbor

Classify the test document as the class of the document “nearest” to the query document (use vector similarity, e.g. Euclidean distance, to find most similar doc)

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Classification by kNN

Classify the test document as the majority class of the k documents “nearest” to the query document.

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Decision tree classifier

Example problem: decide whether to wait for a table at a restaurant, based on the following attributes:1. Alternate: is there an alternative restaurant nearby?

2. Bar: is there a comfortable bar area to wait in?

3. Fri/Sat: is today Friday or Saturday?

4. Hungry: are we hungry?

5. Patrons: number of people in the restaurant (None, Some, Full)

6. Price: price range ($, $$, $$$)

7. Raining: is it raining outside?

8. Reservation: have we made a reservation?

9. Type: kind of restaurant (French, Italian, Thai, Burger)

10. WaitEstimate: estimated waiting time (0-10, 10-30, 30-60, >60)

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Decision tree classifier

Decision tree classifier

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Shall I play tennis today?

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How do we choose the best attribute?

Leaf nodes

Choose next attribute for splitting

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Criterion for attribute selection

• Which is the best attribute?– The one which will result in the smallest tree– Heuristic: choose the attribute that produces

the “purest” nodes• Need a good measure of purity!

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Information Gain

Which test is more informative?

Humidity

=>75%>75% =>20>20

Wind

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Information Gain

Impurity/Entropy (informal)– Measures the level of impurity in a group

of examples

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Impurity

Very impure group Less impure Minimum impurity

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Entropy: a common way to measure impurity

• Entropy =

pi is the probability of class i

Compute it as the proportion of class i in the set.

i

ii pp 2log

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2-Class Cases:

• What is the entropy of a group in which all examples belong to the same class?• entropy = - 1 log21 = 0

• What is the entropy of a group with 50% in either class?• entropy = -0.5 log20.5 – 0.5 log20.5 =1

Minimum impurity

Maximumimpurity

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Information Gain

• We want to determine which attribute in a given set of training feature vectors is most useful for discriminating between the classes to be learned.

• Information gain tells us how useful a given attribute of the feature vectors is.

• We can use it to decide the ordering of attributes in the nodes of a decision tree.

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Calculating Information Gain

996.030

16log

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16

30

14log

30

1422

impurity

787.017

4log

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4

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13log

17

1322

impurity

Entire population (30 instances)17 instances

13 instances

(Weighted) Average Entropy of Children = 615.0391.030

13787.0

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Information Gain= 0.996 - 0.615 = 0.38

391.013

12log

13

12

13

1log

13

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impurity

Information Gain = entropy(parent) – [weighted average entropy(children)]

parententropy

childentropy

childentropy

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e.g. based on information gain

Model Ensembles

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Random Forests

A variant of bagging proposed by Breiman

Classifier consists of a collection of decision tree-structure classifiers.

Each tree cast a vote for the class of input x.

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• A simple algorithm for learning robust classifiers– Freund & Shapire, 1995– Friedman, Hastie, Tibshhirani, 1998

• Provides efficient algorithm for sparse visual feature selection– Tieu & Viola, 2000– Viola & Jones, 2003

• Easy to implement, doesn’t require external optimization tools. Used for many real problems in AI.

Boosting

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• Defines a classifier using an additive model:

Boosting

Strong classifier

Weak classifier

WeightInput featurevector

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• Defines a classifier using an additive model:

• We need to define a family of weak classifiers

Boosting

Strong classifier

Weak classifier

WeightInput featurevector

Selected from a family of weak classifiers

AdaboostInput: training samplesInitialize weights on samples

For T iterations:

Select best weak classifier based on weighted error

Update sample weights

Output: final strong classifier (combination of selected weak classifier predictions)

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Each data point has

a class label:

wt =1and a weight:

+1 ( )

-1 ( )yt =

Boosting• It is a sequential procedure:

xt=1

xt=2

xt

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Toy exampleWeak learners from the family of lines

h => p(error) = 0.5 it is at chance

Each data point has

a class label:

wt =1and a weight:

+1 ( )

-1 ( )yt =

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Toy example

This one seems to be the best

Each data point has

a class label:

wt =1and a weight:

+1 ( )

-1 ( )yt =

This is a ‘weak classifier’: It performs slightly better than chance.

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Toy example

Each data point has

a class label:

wt wt exp{-yt Ht}

We update the weights:

+1 ( )

-1 ( )yt =

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Toy example

Each data point has

a class label:

wt wt exp{-yt Ht}

We update the weights:

+1 ( )

-1 ( )yt =

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Toy example

Each data point has

a class label:

wt wt exp{-yt Ht}

We update the weights:

+1 ( )

-1 ( )yt =

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Toy example

Each data point has

a class label:

wt wt exp{-yt Ht}

We update the weights:

+1 ( )

-1 ( )yt =

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Toy example

The strong (non- linear) classifier is built as the combination of all the weak (linear) classifiers.

f1 f2

f3

f4

AdaboostInput: training samplesInitialize weights on samples

For T iterations:

Select best weak classifier based on weighted error

Update sample weights

Output: final strong classifier (combination of selected weak classifier predictions)

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Boosting for Face Detection

Face detection

features?

classify+1 face

-1 not face

• We slide a window over the image• Extract features for each window• Classify each window into face/non-face

x F(x) y

? ?

What is a face?

• Eyes are dark (eyebrows+shadows)• Cheeks and forehead are bright.• Nose is bright

Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04

Basic feature extraction

• Information type:– intensity

• Sum over:– gray and white rectangles

• Output: gray-white• Separate output value for

– Each type– Each scale– Each position in the window

• FEX(im)=x=[x1,x2,…….,xn]

Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04

x120x357

x629 x834

Decision trees

• Stump: – 1 root– 2 leaves

• If xi > athen positive

else negative

• Very simple• “Weak classifier”

Paul Viola, Michael Jones, Robust Real-time Object Detection, IJCV 04

x120x357

x629 x834

Summary: Face detection

• Use decision stumps as week classifiers

• Use boosting to build a strong classifier

• Use sliding window to detect the face

x120x357

x629 x834

X234>1.3

-1Non-face

+1 Face

YesNo

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Discriminant Function• It can be arbitrary functions of x, such as:

Nearest Neighbor

Decision Tree

LinearFunctions