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09_Classification Models
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Logistic regression Bayesian logistic regression Non-linear logistic regression Kernelization and Gaussian process classification Incremental fitting, boosting and trees Multi-class classification Random classification trees Non-probabilistic classification Applications

1.Computer vision: models, learning and inference Chapter 9 Classification Models

2.Structure 2 2 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Logistic regression Bayesian logistic regression Non-linear logistic regression Kernelization and Gaussian process classification Incremental fitting, boosting and trees Multi-class classification Random classification trees Non-probabilistic classification Applications

3.Models for machine vision 3 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

4.Example application: Gender Classification 4 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

5.Type 1: Model Pr( w | x ) - Discriminative How to model Pr( w | x )? Choose an appropriate form for Pr( w ) Make parameters a function of x Function takes parameters q that define its shape Learning algorithm : learn parameters q from training data x , w Inference algorithm : just evaluate Pr( w|x ) 5 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

6.Logistic Regression Consider two class problem. Choose Bernoulli distribution over world. Make parameter l a function of x Model activation with a linear function creates number between . Maps to with 6 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

7.Two parameters Learning by standard methods (ML,MAP, Bayesian) Inference: Just evaluate Pr( w|x ) 7 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

8.Neater Notation To make notation easier to handle, we Attach a 1 to the start of every data vector Attach the offset to the start of the gradient vector f New model: 8 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

9.Logistic regression 9 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

10.Maximum Likelihood 10 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Take logarithm Take derivative:

11.Derivatives Unfortunately, there is no closed form solution– we cannot get an expression for f in terms of x and w Have to use a general purpose technique: “iterative non-linear optimization” 11 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

12.Optimization Goal: How can we find the minimum? Basic idea: Start with estimate Take a series of small steps to Make sure that each step decreases cost When can’t improve, then must be at minimum Cost function or Objective function 12 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

13.Local Minima 13 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

14.Convexity If a function is convex, then it has only a single minimum. Can tell if a function is convex by looking at 2 nd derivatives 14 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

15.15 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

16.Gradient Based Optimization Choose a search direction s based on the local properties of the function Perform an intensive search along the chosen direction. This is called line search Then set 16 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

17.Gradient Descent Consider standing on a hillside Look at gradient where you are standing Find the steepest direction downhill Walk in that direction for some distance (line search) 17 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

18.Finite differences What if we can’t compute the gradient? Compute finite difference approximation: where e j is the unit vector in the j th direction 18 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

19.Steepest Descent Problems Close up 19 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

20.Second Derivatives In higher dimensions, 2 nd derivatives change how much we should move in the different directions: changes best direction to move in. 20 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

21.Newton’s Method Approximate function with Taylor expansion Take derivative Re-arrange 21 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Adding line search (derivatives taken at time t )

22.Newton’s Method Matrix of second derivatives is called the Hessian. Expensive to compute via finite differences. If positive definite, then convex 22 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

23.Newton vs. Steepest Descent 23 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

24.Line Search Gradually narrow down range 24 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

25.Optimization for Logistic Regression Derivatives of log likelihood: Positive definite! 25 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

26.26 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

27.Maximum likelihood fits 27 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

28.Structure 28 28 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Logistic regression Bayesian logistic regression Non-linear logistic regression Kernelization and Gaussian process classification Incremental fitting, boosting and trees Multi-class classification Random classification trees Non-probabilistic classification Applications

29.29 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince

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