Jeff Howbert Introduction to Machine Learning Winter

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1 Classification / Regression Support Vector Machines Jeff Howbert Introduction to Machine Learning Winter

2 Topics SVM classifiers for linearly separable classes SVM classifiers for non-linearly separable classes SVM classifiers for nonlinear decision boundaries kernel functions Other applications of SVMs Software Jeff Howbert Introduction to Machine Learning Winter

3 Linearly separable classes Goal: find a linear decision boundary (hyperplane) that separates the classes Jeff Howbert Introduction to Machine Learning Winter

4 One possible solution Jeff Howbert Introduction to Machine Learning Winter

5 Another possible solution Jeff Howbert Introduction to Machine Learning Winter

6 Other possible solutions Jeff Howbert Introduction to Machine Learning Winter

7 Which h one is better? B1 or B2? How do you define better? Jeff Howbert Introduction to Machine Learning Winter

8 Hyperplane that maximizes the margin will have better generalization => B1 is better than B2 Jeff Howbert Introduction to Machine Learning Winter

9 B 1 test sample B 2 b 21 b 22 margin b 11 Hyperplane that maximizes the margin will have better generalization => B1 is better than B2 Jeff Howbert Introduction to Machine Learning Winter b 12

10 B 1 test sample B 2 b 21 b 22 margin b 11 Hyperplane that maximizes the margin will have better generalization => B1 is better than B2 Jeff Howbert Introduction to Machine Learning Winter b 12

11 B 1 W w x + b = 0 w x + b = 1 w x + b = +1 b 11 y i = f ( x ) = + 1 if w x + b 1 2 margin = 1 if w x + b 1 w Jeff Howbert Introduction to Machine Learning Winter b 12

12 We want to maximize: margin = 2 w Which is equivalent to minimizing: L( w ) = w 2 2 But subject to the following constraints: y i + 1 if w x + b 1 = f ( x) = 1 if w x + b 1 This is a constrained convex optimization problem Solve with numerical approaches, e.g. quadratic programming Jeff Howbert Introduction to Machine Learning Winter

13 Solving for w that gives maximum margin: 1. Combine objective function and constraints into new objective function, using Lagrange multipliers λ i N 1 2 L primal = w λi ( yi ( w xi + b ) 1 ) 2 i= 1 2. To minimize this Lagrangian, we take derivatives of w and b and set them to 0: L L p w p = 0 w = λ i y ix N N i= 1 = 0 λ y b i= 1 Jeff Howbert Introduction to Machine Learning Winter y i i = 0 i

14 Solving for w that gives maximum margin: 3. Substituting and rearranging gives the dual of the Lagrangian: L N 1 λ λ λ y y x x dual = i i j i y j i j i= 1 2 i, j which we try to maximize (not minimize). 4. Once we have the λ i, we can substitute into previous equations to get w and b. 5. This defines w and b as linear combinations of the training data. Jeff Howbert Introduction to Machine Learning Winter

15 Optimizing the dual is easier. Function of λ i only, not λ i and w. Convex optimization guaranteed to find global optimum. Most of the λ i go to zero. The x i for which λ i 0 are called the support vectors. These support (lie on) the margin boundaries. The x i for which λ i = 0 lie away from the margin boundaries. They are not required for defining the maximum margin hyperplane. Jeff Howbert Introduction to Machine Learning Winter

16 Example of solving for maximum margin hyperplane Jeff Howbert Introduction to Machine Learning Winter

17 What if the classes are not linearly separable? Jeff Howbert Introduction to Machine Learning Winter

18 Now which one is better? B1 or B2? How do you define better? Jeff Howbert Introduction to Machine Learning Winter

19 What if the problem is not linearly separable? Solution: introduce slack variables Need to minimize: Subject to: y i = f ( x ) = 2 w = + N k L( w) C ξi 2 i= if w x + b 1 + ξ i 1 if w x + b 1 + ξ i C is an important hyperparameter, whose value is usually optimized by cross-validation. Jeff Howbert Introduction to Machine Learning Winter

20 Slack variables for nonseparable data Jeff Howbert Introduction to Machine Learning Winter

21 What if decision boundary is not linear? Jeff Howbert Introduction to Machine Learning Winter

22 Solution: nonlinear transform of attributes Φ :[ x 1, x2] [ x1,( x1 + x 2 ) 4 ] Jeff Howbert Introduction to Machine Learning Winter

23 Solution: nonlinear transform of attributes 2 Φ :[ x1, x2] [( x1 x1 ),( x2 x2)] 2 Jeff Howbert Introduction to Machine Learning Winter

24 Issues with finding useful nonlinear transforms Not feasible to do manually as number of attributes grows (i.e. any real world problem) Usually involves transformation to higher dimensional space increases computational burden of SVM optimization curse of dimensionality With SVMs, can circumvent all the above via the kernel trick Jeff Howbert Introduction to Machine Learning Winter

25 Kernel trick Support vector machines Don t need to specify the attribute transform Φ( x ) Only need to know how to calculate the dot product of any two transformed samples: k( x 1, x 2 ) = Φ( x 1 ) Φ( x 2 ) The kernel function k is substituted into the dual of the Lagrangian, allowing determination ti of a maximum margin hyperplane in the (implicitly) transformed space Φ( x ) All subsequent calculations, including predictions on test samples, are done using the kernel in place of Φ( x 1 ) Φ( x 2 ) Jeff Howbert Introduction to Machine Learning Winter

26 Common kernel functions for SVM linear k x, x ) ( 1 2 = x1 x2 polynomial k( x1, x2) = ( γ x1 x2 + c) d ( ) 2 Gaussian or radial basis k( x, x ) = exp γ x x sigmoid k ( x, x2) = tanh( γ x1 x2 + 1 c ) Jeff Howbert Introduction to Machine Learning Winter

27 For some kernels (e.g. Gaussian) the implicit transform Φ( x ) is infinite-dimensional! But calculations with kernel are done in original space, so computational burden and curse of dimensionality aren t a problem. Jeff Howbert Introduction to Machine Learning Winter

28 Jeff Howbert Introduction to Machine Learning Winter

29 Applications of SVMs to machine learning Classification binary multiclass one-class Regression Transduction (semi-supervised learning) Ranking Clustering Structured labels Jeff Howbert Introduction to Machine Learning Winter

30 Software SVM light org/ libsvm includes MATLAB / Octave interface Jeff Howbert Introduction to Machine Learning Winter

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