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**Introduction to Support Vector Machines (SVM)**

By Debprakash Patnaik M.E (SSA)

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**Introduction SVMs provide a learning technique for Pattern Recognition**

Regression Estimation Solution provided SVM is Theoretically elegant Computationally Efficient Very effective in many Large practical problems It has a simple geometrical interpretation in a high-dimensional feature space that is nonlinearly related to input space By using kernels all computations keep simple. It contains ANN, RBF and Polynomial classifiers as special cases.

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History The Study on Statistical Learning Theory was started in the 1960s by Vapnik Statistical Learning Theory is the theory about Machine Learning Principle from a small sample size. Support Vector Machine is a practical learning method based on Statistical Learning Theory A simple SVM could beat a sophisticated neural networks with elaborate features in a handwriting recognition task.

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Learning Machine A bound on the Generalization Performance of Learning Machine Expected Risk: Empirical Risk: is the VC dimension, a measure of the notion of capacity of a classifier.

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VC Dimension The VC dimension is a property of a set of functions , and can be defined for various classes of function . The VC dimension for the set of functions is defined as the maximum number of training points that can be shattered by VC dimension gives concreteness to the notion of the capacity of a given set of functions. The number of parameters of Learning Machines is not proportional to the VC dimension.

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**VC Dimension – An example**

The VC-Dimension of the set of oriented hyperplanes in Rn is (n+1)

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**Structural Risk Minimization**

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Two Approaches Goal: To find a trained machine in the series whose sum of empirical risk and VC confidence is minimal. Neural Network Fix the VC confidence and minimize the empirical risk Support Vector Machine Fix the empirical risk and minimize the VC confidence

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The Two Class Problem Several decision boundaries can separate these two classes. Perceptron Algorithm learns any separating hyperplane. SVM learns the best separating hyperplane. Class 1 Class 2

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**Simple Perceptron learning Algorithm**

Perceptron Algorithm Class 2 Class 1 Simple Perceptron learning Algorithm

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**Finding the Optimal Separating Hyperplane in SVM**

SVM Algorithm Class 2 Support Vectors Optimal Separating Hyperplane Class 1 Finding the Optimal Separating Hyperplane in SVM

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**Decision Boundary We should maximize the margin, m**

The decision boundary/hyperplane should be as far away from the data of both classes as possible. We should maximize the margin, m Class 2 m Class 1

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**The Optimization Problem**

Let {x1, ..., xn} be our data set And let yi Î {1,-1} be the class label of xi The decision boundary should classify all points correctly Þ A constrained optimization problem

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**Dual Formulation The Lagrangian for this problem is**

where are the Lagrange multipliers Quadratic cost Optimization and linear constraints. The Kuhn-Tucker Conditions for the problem where (w*,b*) is the global solution of L and µ* is the optimal Lagrange multiplier.

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**Support Vectors Complimentary Slackness condition We must have**

Support Vectors are the set of xi’s that have µ*i>0

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**The Dual Problem We can transform the problem to its dual**

subject to This is a quadratic programming (QP) problem w*,b* can be recovered by Let x(1) and x(-1) be two S.V. Then b = -1/2( w^T x(1) + w^T x(-1) )

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**A Geometrical Interpretation**

µ 10=0 Class 2 µ8=0.6 µ7=0 µ5=0 µ2=0 µ1=0.8 µ4=0 So, if change internal points, no effect on the decision boundary µ6=1.4 µ9=0 µ3=0 Class 1

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Some Notes There are theoretical upper bounds on the error on unseen data for SVM The larger the margin, the smaller the bound The smaller the number of SV, the smaller the bound Note that in both training and testing, the data are referenced only as inner product, xTy This is important for generalizing to the non-linear case

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**If Not Linearly Separable**

We allow “error” xi in classification Class 2 Class 1

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**Soft Margin Hyperplane**

Define xi=0 if there is no error for xi xi are just “slack variables” in optimization theory We want to minimize C : tradeoff parameter between error and margin The optimization problem becomes

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**The New Optimization Problem**

The dual of the problem is w is also recovered as The only difference with the linear separable case is that there is an upper bound C on ai A QP solver can be used to find µi ‘s Note also, everything is done by inner-products

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**Extension to Non-linear Decision Boundary**

Key idea: transform xi to a higher dimensional space to “make classes linearly separable” Input space: the space xi are in Feature space: the space of f(xi) after transformation Why transform? Linear operation in the feature space is equivalent to non-linear operation in input space The classification task can be “easier” with a proper transformation. Example: XOR XOR: x_1, x_2, and we want to transform to x_1^2, x_2^2, x_1 x_2 It can also be viewed as feature extraction from the feature vector x, but now we extract more feature than the number of features in x.

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Higher Dimensions Project the data to high dimensional space where it is linearly separable and then we can use linear SVM – (Using Kernels) (1,0) (0,0) (0,1) + - -1 +1 + -

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The XOR problem X = ( x1, x2 ) Z = ( x1, x2, x1x2 )

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**Extension to Non-linear Decision Boundary**

Possible problem of the transformation High computation burden and hard to get a good estimate SVM solves these two issues simultaneously Kernel tricks for efficient computation Minimize ||w||2 can lead to a “good” classifier Feature space Input space Φ: x → φ(x)

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What is Kernel?

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**Example Transformation**

Define the kernel function K (x,y) as Consider the following transformation The inner product can be computed by K without going through the map f(.)

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Kernel Trick The relationship between the kernel function K and the mapping f(.) is This is known as the kernel trick In practice, we specify K, thereby specifying f(.) indirectly, instead of choosing f(.) K (x,y) needs to satisfy Mercer condition in order for f(.) to exist

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**Examples of Kernel Functions**

Polynomial kernel with degree d Radial basis function kernel with width s Closely related to radial basis function neural networks Sigmoid with parameter k and q It does not satisfy the Mercer condition on all k and q Despite violating Mercer condition, the sigmoid kernel function can still work

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(x1,x2) y = -1 y = +1

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**Optimization Algorithms**

Most popular optimization algorithms for SVMs are SMO [Platt ’99] and SVMlight [Joachims’ 99], both use decomposition to hill-climb over a subset of µi’s at a time. Idea behind SMO Adjusting only 2 µi’s at each step All µi’s are initialized to be zero

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**SVM vs. Neural Networks SVM Neural Networks Relatively new concept**

Nice Generalization properties Hard to learn – learned in batch modes using QP techniques Using kernels can learn very complex functions Neural Networks Generalizes well but doesn’t have mathematical foundation Can easily be learnt in incremental fashion To learn complex function – use complex multi layer structure.

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**Example of Non-linear SVM**

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Results

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**A Nonlinear Kernel Application**

Checkerboard Training Set: 1000 Points in R2 Separate 486 Asterisks from 514 Dots

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Previous Work

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Polynomial Kernel

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**SVM Applications Pattern Recognition**

handwriting recognition 3D object recognition speaker identification face detection text categorization bio-informatics Regression estimation or function learning. More…

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References [1] C.J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, 1998 [2] P.S. Sastry, “An Introduction to Support Vector Machine” [3] J. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines”, 1999

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