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| | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
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| | === Slides === | | === Slides === |
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| − | * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/PAC.pdf Slides in pdf]. | + | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/pac-learning.pdf Slides in pdf], [http://www.cs.cmu.edu/~wcohen/10-601/pac-learning.pptx Slides in Powerpoint], |
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| − | [http://curtis.ml.cmu.edu/w/courses/images/8/8d/Lecture10-pac.pdf Slides in PDF] | |
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| − | [http://curtis.ml.cmu.edu/w/courses/images/0/0f/Lecture10-pac-annotated.pdf Annotated Slides in PDF]
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| | === Readings === | | === Readings === |
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| − | the PAC learning, VC dimension, etc. in Tom Mitchell 's Chapter 7
| + | * Mitchell Chapter 7 |
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| − | === An example of PAC learnability of Boolean literals : Learning a Boolean Conjunction ===
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| − | (from http://www.cis.temple.edu/~giorgio/cis587/readings/pac.html)
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| − | We can efficiently PAC learn concepts that are represented as the conjunction of boolean literals (i.e. positive or negative boolean variables). Here is a learning algorithm:
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| − | 1. Start with an hypothesis h which is the conjunction of each variable and its negation x1 & ~x1 & x2 & ~x2 & .. & xn & ~xn.
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| − | 2. Do nothing with negative instances.
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| − | 3. On a positive instance a, eliminate in h ~xi if ai is positive, eliminate xi if ai is negative. For example if a positive instance is 01100 then eliminate x1, ~x2, ~x3, x4, and x5.
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| − | In this algorithm h, as domain, is non-decreasing and at all times contained in the set denoted by c. [By induction: certainly true initially, ..]. We will have an error when h contains a literal z which is not in c.
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| − | We compute first the probability that a literal z is deleted from h because of one specific positive example. Clearly this probability is 0 if z occurs in c, and if ~z is in c the probability is 1. At issue are the z where neither z nor ~z is in c.
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| − | We would like to eliminate both of them from h. If one of the two remains, we have an error for an instance a that is positive for c and negative for h. Let's call these literals, '''free''' literals.
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| − | We have:
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| − | '''error(h) is less than or equal to the Sum of the probabilities of the free literals z in h not to be eliminated by one positive example.'''
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| − | Since there are at most 2*n literals in h, if h is a bad hypothesis, i.e. an hypothesis with error greater than epsilon, we will have
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| − | '''Probability[free literal z is eliminated from h by one positive example] > epsilon/(2*n)'''
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| − | From this we obtain :
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| − | '''Probability[free literal z survives one positive example] = 1 - Probability[free literal z is eliminated from h by one positive example] < (1 - epsilon/(2*n))'''
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| − | '''Probability[free literal z survives m positive examples] < (1 - epsilon/(2*n))^m'''
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| − | '''Probability[some free literal z survives m positive examples] < 2n*(1 - epsilon/(2*n))^m < 2n*(e^(-epsilon/(2*n)))^m = 2n*e^(-(m*epsilon)/(2*n))'''
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| − | That is
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| − | '''m > (2*n/epsilon)*(ln(1/delta)+n*ln(2))'''
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| | === What you should remember === | | === What you should remember === |
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| − | * Relationships between sample complexity, error bound, and “capacity” of chosen hypothesis space | + | * Definition of pac-learnability. |
| − | * Within the PAC learning setting, we can bound the probability that learner will output hypothesis with given error | + | * Definition of sample complexity vs time complexity |
| − | ** For ANY consistent learner (case where c in H) | + | * How sample complexity grows with 1/epsilon, 1/delta, and |H| |
| − | ** For ANY “best fit” hypothesis (agnostic learning, where perhaps c not in H)
| + | ** in the noise-free case. |
| − | | + | ** in the "agnostic" setting, where noise is present and the learner outputs the smallest-error-rate hypothesis. |
| − | * VC dimension as measure of complexity of H | + | * The definition of VC-dimension and shattering |
| | + | * How VC dimension relates to sample complexity |
