Support Vector Machine. Tutorial. (and Statistical Learning Theory)


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1 Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America 4 Independence Way, Princeton, USA.
2 1 Support Vector Machines: history SVMs introduced in COLT92 by Boser, Guyon & Vapnik. Became rather popular since. Theoretically well motivated algorithm: developed from Statistical Learning Theory (Vapnik & Chervonenkis) since the 60s. Empirically good performance: successful applications in many fields(bioinformatics,tet,imagerecognition,...)
3 2 Support Vector Machines: history II Centralized website: Several tetbooks, e.g. An introduction to Support Vector Machines by Cristianini and ShaweTaylor is one. A large and diverse community work on them: from machine learning, optimization, statistics, neural networks, functional analysis, etc.
4 3 Support Vector Machines: basics [Boser, Guyon, Vapnik 92],[Cortes & Vapnik 95] margin margin Nice properties: conve, theoretically motivated, nonlinear with kernels..
5 4 Preliminaries: Machine learning is about learning structure from data. Although the class of algorithms called SVM s can do more, in this talk we focus on pattern recognition. So we want to learn the mapping: X Y,where Xis some object and y Yis a class label. Let s take the simplest case: 2class classification. So: R n, y {±1}.
6 5 Eample: Suppose we have 50 photographs of elephants and 50 photos of tigers. vs. We digitize them into piel images, so we have R n where n =10, 000. Now, given a new (different) photograph we want to answer the question: is it an elephant or a tiger? [we assume it is one or the other.]
7 6 Training sets and prediction models input/output sets X, Y training set ( 1,y 1 ),...,( m,y m ) generalization : given a previously seen X, find a suitable y Y. i.e., want to learn a classifier: y = f(, α),whereα are the parameters of the function. For eample, if we are choosing our model from the set of hyperplanes in R n,thenwehave: f(, {w, b}) =sign(w + b).
8 7 Empirical Risk and the true Risk We can try to learn f(, α) by choosing a function that performs well on training data: R emp (α) = 1 m m l(f( i,α),y i ) = Training Error i=1 where l is the zeroone loss function, l(y, ŷ) =1,ify ŷ,and0 otherwise. R emp is called the empirical risk. By doing this we are trying to minimize the overall risk: R(α) = l(f(, α), y)dp (, y) = Test Error where P(,y) is the (unknown) joint distribution function of and y.
9 8 Choosing the set of functions What about f(, α) allowing all functions from X to {±1}? Training set ( 1,y 1 ),...,( m,y m ) X {±1} Test set 1,..., m X, such that the two sets do not intersect. For any f there eists f : 1. f ( i )=f( i ) for all i 2. f ( j ) f( j ) for all j Based on the training data alone, there is no means of choosing which function is better. On the test set however they give different results. So generalization is not guaranteed. = a restriction must be placed on the functions that we allow.
10 9 Empirical Risk and the true Risk Vapnik & Chervonenkis showed that an upper bound on the true risk can be given by the empirical risk + an additional term: h(log( 2m h R(α) R emp (α)+ +1) log( η 4 ) m where h is the VC dimension of the set of functions parameterized by α. The VC dimension of a set of functions is a measure of their capacity or compleity. If you can describe a lot of different phenomena with a set of functions then the value of h is large. [VC dim = the maimum number of points that can be separated in all possible ways by that set of functions.]
11 10 VC dimension: The VC dimension of a set of functions is the maimum number of points that can be separated in all possible ways by that set of functions. For hyperplanes in R n, the VC dimension can be shown to be n +1.
12 11 VC dimension and capacity of functions Simplification of bound: Test Error Training Error + Compleity of set of Models Actually, a lot of bounds of this form have been proved (different measures of capacity). The compleity function is often called a regularizer. If you take a high capacity set of functions (eplain a lot) you get low training error. But you might overfit. If you take a very simple set of models, you have low compleity, but won t get low training error.
13 12 Capacity of a set of functions (classification) [Images taken from a talk by B. Schoelkopf.]
14 13 Capacity of a set of functions (regression) y sine curve fit hyperplane fit true function
15 14 Controlling the risk: model compleity Bound on the risk Confidence interval Empirical risk (training error) h 1 h* h n S1 S* S n
16 15 Capacity of hyperplanes Vapnik & Chervonenkis also showed the following: Consider hyperplanes (w ) =0where w is normalized w.r.t a set of points X such that: min i w i =1. The set of decision functions f w () =sign(w ) defined on X such that w A has a VC dimension satisfying h R 2 A 2. where R is the radius of the smallest sphere around the origin containing X. = minimize w 2 and have low capacity = minimizing w 2 equivalent to obtaining a large margin classifier
17 <w, > + b > 0 <w, > + b < 0 w { <w, > + b = 0}
18 { <w, > + b = 1} y i = 1 w { <w, > + b = +1} 2 1, y i = +1 Note: <w, 1 > + b = +1 <w, 2 > + b = 1 => <w, ( 1 2 )> = 2 => < w, w ( 1 2 ) > 2 = w { <w, > + b = 0}
19 16 Linear Support Vector Machines (at last!) So, we would like to find the function which minimizes an objective like: Training Error + Compleity term We write that as: 1 m m l(f( i,α),y i ) + Compleity term i=1 For now we will choose the set of hyperplanes (we will etend this later), so f() =(w )+b: 1 m m l(w i + b, y i ) + w 2 i=1 subject to min i w i =1.
20 17 Linear Support Vector Machines II That function before was a little difficult to minimize because of the step function in l(y, ŷ) (either 1 or 0). Let s assume we can separate the data perfectly. Then we can optimize the following: Minimize w 2, subject to: (w i + b) 1, if y i =1 (w i + b) 1, if y i = 1 The last two constraints can be compacted to: y i (w i + b) 1 This is a quadratic program.
21 18 SVMs : nonseparable case To deal with the nonseparable case, one can rewrite the problem as: Minimize: subject to: w 2 + C m i=1 ξ i y i (w i + b) 1 ξ i, ξ i 0 This is just the same as the original objective: 1 m m l(w i + b, y i ) + w 2 i=1 ecept l is no longer the zeroone loss, but is called the hingeloss : l(y, ŷ) =ma(0, 1 yŷ). This is still a quadratic program!
22 margin ξ i
23 19 Support Vector Machines  Primal Decision function: Primal formulation: f() =w + b min P (w, b)= 1 2 w 2 + C H 1 [ y i f( i )] } {{ } i } {{ } maimize margin minimize training error Ideally H 1 would count the number of errors, approimate with: H 1(z) Hinge Loss H 1 (z) =ma(0, 1 z) 0 z
24 20 SVMs : nonlinear case Linear classifiers aren t comple enough sometimes. SVM solution: Map data into a richer feature space including nonlinear features, then construct a hyperplane in that space so all other equations are the same! Formally, preprocess the data with: Φ() and then learn the map from Φ() to y: f() =w Φ()+b.
25 21 SVMs : polynomial mapping Φ:R 2 R 3 ( 1, 2 ) (z 1,z 2,z 3 ):=( 2 1, (2) 1 2, 2 2) 1 2 z 1 z 3 z 2
26 22 SVMs : nonlinear case II For eample MNIST handwriting recognition. 60,000 training eamples, test eamples, Linear SVM has around 8.5% test error. Polynomial SVM has around 1% test error
27 23 SVMs : full MNIST results Classifier Test Error linear 8.4% 3nearestneighbor 2.4% RBFSVM 1.4 % Tangent distance 1.1 % LeNet 1.1 % Boosted LeNet 0.7 % Translation invariant SVM 0.56 % Choosing a good mapping Φ( ) (encoding prior knowledge + getting right compleity of function class) for your problem improves results.
28 24 SVMs : the kernel trick Problem: the dimensionality of Φ() can be very large, making w hard to represent eplicitly in memory, and hard for the QP to solve. The Representer theorem (Kimeldorf & Wahba, 1971) shows that (for SVMs as a special case): m w = α i Φ( i ) for some variables α. Instead of optimizing w directly we can thus optimize α. The decision rule is now: f() = i=1 m α i Φ( i ) Φ()+b i=1 We call K( i,)=φ( i ) Φ() the kernel function.
29 25 Support Vector Machines  kernel trick II We can rewrite all the SVM equations we saw before, but with the w = m i=1 α iφ( i ) equation: Decision function: f() = i α i Φ( i ) Φ()+b = i α i K( i, )+b Dual formulation: min P (w, b)= 1 m 2 α i Φ( i ) 2 + C H 1 [ y i f( i )] i=1 i } {{ } } {{ } maimize margin minimize training error
30 26 Support Vector Machines  Dual But people normally write it like this: Dual formulation: min α D(α) = 1 2 α i α j Φ( i ) Φ( j ) i,j i y i α i s.t. Èi α i=0 0 y i α i C Dual Decision function: f() = i α i K( i, )+b Kernel function K(, ) is used to make (implicit) nonlinear feature map, e.g. Polynomial kernel: K(, )=( +1) d. RBF kernel: K(, )=ep( γ 2 ).
31 27 PolynomialSVMs The kernel K(, )=( ) d gives the same result as the eplicit mapping + dot product that we described before: Φ:R 2 R 3 ( 1, 2 ) (z 1,z 2,z 3 ):=( 2 1, (2) 1 2, 2 2) Φ(( 1, 2 ) Φ(( 1, 2)=( 2 1, (2) 1 2, 2 2) ( 2 1, (2) 1 2, 2 2) is the same as: = K(, )=( ) 2 =(( 1, 2 ) ( 1, 2)) 2 =( ) 2 = Interestingly, if d is large the kernel is still only requires n multiplications to compute, whereas the eplicit representation may not fit in memory!
32 28 RBFSVMs The RBF kernel K(, )=ep( γ 2 ) is one of the most popular kernel functions. It adds a bump around each data point: m f() = α i ep( γ i 2 )+b i=1 Φ.. ' Φ() Φ(') Using this one can get stateoftheart results.
33 29 SVMs : more results There is much more in the field of SVMs/ kernel machines than we could cover here, including: Regression, clustering, semisupervised learning and other domains. Lots of other kernels, e.g. string kernels to handle tet. Lots of research in modifications, e.g. to improve generalization ability, or tailoring to a particular task. Lots of research in speeding up training. Please see tet books such as the ones by Cristianini & ShaweTaylor or by Schoelkopf and Smola.
34 30 SVMs : software Lots of SVM software: LibSVM (C++) SVMLight (C) As well as complete machine learning toolboes that include SVMs: Torch (C++) Spider (Matlab) Weka (Java) All available through
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