Support Vector Machine
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This document provides an overview of support vector machines (SVMs). It discusses how SVMs can be used to perform classification tasks by finding optimal separating hyperplanes that maximize the margin between different classes. The document outlines how SVMs solve an optimization problem to find these optimal hyperplanes using techniques like Lagrange duality, kernels, and soft margins. It also covers model selection methods like cross-validation and discusses extensions of SVMs to multi-class classification problems.
Support Vector Machine
- 1. Support Vector MachineShao-Chuan Wang1
- 2. Support Vector Machine1D Classification Problem: how will you separate these data?(H1, H2, H3?)2H1H2H3x0
- 3. Support Vector Machine2D Classification Problem: which H is better?3
- 4. Max-Margin ClassifierFunctional MarginGeometric Margin4We feel more confident when functional margin is largerNote that scaling on w, b won’t change the plane.Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 5. Maximize marginsOptimization problem: maximize minimal geometric margin under constraints.Introduce scaling factor such that5Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 6. Optimization problem subject to constraintsMaximize f(x, y), subject to constraint g(x, y) = c6-> Lagrange multiplier method
- 7. Lagrange dualityPrimal optimization problem:GeneralizedLagrangian methodPrimal optimization problem (equivalent form)Dual optimization problem:7Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 8. Dual ProblemThe necessary conditions that equality holds:f, giare convex, and hi are affine.KKT conditions.8Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 9. Optimal margin classifiersIts LagrangianIts dual problem9Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 10. Support Vector Machine (cont’d)If not linearly separable, we canFind a nonlinear solutionTechnically, it’s a linear solution in higher-order space Kernel Trick26
- 11. Kernel and feature mappingKernel:Positive semi-definiteSymmetricFor example:Loose Intuition“similarity” between features11Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 12. Soft Margin (L1 regularization)12C = ∞ leads to hard margin SVM, Rychetsky (2001)Andrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 13. Why doesn’t my model fit well on test data ?13
- 14. Bias/variance tradeoffunderfitting(high bias) overfitting(high variance) Training Error = Generalization Error =14In-sample errorOut-of-sample errorAndrew Ng. Part V Support Vector Machines. CS229 Lecture Notes (2008).
- 15. Bias/variance tradeoff15T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer series in statistics. Springer, New York, 2001.
- 16. Is training error a good estimator of generalization error?16
- 17. Chernoff bound (|H|=finite)Lemma: Assume Z1, Z2, …, Zmare drawn iid from Bernoulli(φ), and and let γ > 0 be fixed. Then, based on this lemma, one can find, with probability 1-δ(k = # of hypotheses)17Andrew Ng. Part VI Learning Theory. CS229 Lecture Notes (2008).
- 18. Chernoff bound (|H|=infinite)VC Dimension d : The size of largest set that H can shatter.e.g. H = linear classifiersin 2-DVC(H) = 3With probability at least 1-δ,18Andrew Ng. Part VI Learning Theory. CS229 Lecture Notes (2008).
- 19. Model SelectionCross Validation: Estimator of generalization error
- 20. K-fold: train on k-1 pieces, test on the remaining (here we will get one test error estimation). Average k test error estimations, say, 2%. Then 2% is the estimation of generalization error for this machine learner.Leave-one-out cross validation (m-fold, m = training sample size)19traintrainvalidatetraintraintrain
- 21. Model SelectionLoop possible parameters:Pick one set of parameter, e.g. C = 2.0Do cross validation, get a error estimationPick the Cbest (with minimal error estimation) as the parameter20
- 22. Multiclass SVMOne against oneThere are binary SVMs. (1v2, 1v3, …)To predict, each SVM can vote between 2 classes.One against allThere are k binary SVMs. (1 v rest, 2 v rest, …)To predict, evaluate , pick the largest.Multiclass SVM by solving ONE optimization problem21K = 135321123456K = 3poll Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. JMLR, 2, 265-292.
- 23. Multiclass SVM (2/2)DAGSVM (Directed Acyclic Graph SVM)22
- 24. An Example: image classificationProcess23K = 61/4 3/41 0:49 1:25 …1 0:49 1:25 …： ：2 0:49 1:25 …：Test DataAccuracy
- 25. An Example: image classificationResultsRun Multi-class SVM 100 times for both (linear/Gaussian).Accuracy Histogram24