Computational Learning Theory: Shattering and VC Dimensions. Machine Learning. Spring The slides are mainly from Vivek Srikumar
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1 Computational Learning Theory: Shattering and VC Dimensions Machine Learning Spring 2018 The slides are mainly from Vivek Srikumar 1
2 This lecture: Computational Learning Theory The Theory of Generalization Probably Approximately Correct (PAC) learning Positive and negative learnability results Agnostic Learning Shattering and the VC dimension 2
3 Infinite Hypothesis Space The previous analysis was restricted to finite hypothesis spaces Some infinite hypothesis spaces are more expressive than others E.g., Rectangles, 17 sides convex polygons vs. general convex polygons Linear threshold function vs. a combination of LTUs Need a measure of the expressiveness of an infinite hypothesis space other than its size The VapnikChervonenkis dimension (VC dimension) provides such a measure What is the expressive capacity of a set of functions? Analogous to H, there are bounds for sample complexity using VC(H) 3
4 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 4
5 Learning Rectangles Assume the target function is an axis parallel rectangle Y Points outside are negative Points outside are negative Points inside are positive Points outside are negative Points outside are negative X 5
6 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 6
7 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 7
8 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 8
9 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 9
10 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 10
11 Learning Rectangles Assume the target function is an axis parallel rectangle Y X 11
12 Learning Rectangles Assume the target function is an axis parallel rectangle Y Will we be able to learn the target rectangle? X 12
13 Let s think about expressivity of functions There are four ways to label two points And it is possible to draw a line that separates positive and negative points in all four cases Linear functions are expressive enough to shatter 2 points What about fourteen points? 13
14 Shattering 14
15 Shattering 15
16 Shattering This particular labeling of the points can not be separated by any line 16
17 Shattering Linear functions are not expressive to shatter fourteen points Because there is a labeling that can not be separated by them Of course, a more complex function could separate them 17
18 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Intuition: A rich set of functions shatters large sets of points 18
19 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Intuition: A rich set of functions shatters large sets of points Example 1: Hypothesis class of left bounded intervals on the real axis: [0,a) for some real number a>0 0 a 19
20 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Intuition: A rich set of functions shatters large sets of points Example 1: Hypothesis class of left bounded intervals on the real axis: [0,a) for some real number a>0 0 a 0 a Sets of two points cannot be shattered That is: given two points, you can label them in such a way that no concept in this class will be consistent with their labeling 20
21 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 2: Hypothesis class is the set of intervals on the real axis: [a,b],for some real numbers b>a 21
22 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 2: Hypothesis class is the set of intervals on the real axis: [a,b],for some real numbers b>a a b 22
23 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 2: Hypothesis class is the set of intervals on the real axis: [a,b],for some real numbers b>a a b a b 23
24 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 2: Hypothesis class is the set of intervals on the real axis: [a,b],for some real numbers b>a a b a b All sets of one or two points can be shattered But sets of three points cannot be shattered Proof? Enumerate all possible three points 24
25 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 3: Half spaces in a plane 25
26 Shattering Definition: A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Example 3: Half spaces in a plane Can one point be shattered? Two points? Three points? Can any three points be shattered? 26
27 Half spaces on a plane: 3 points Eric CMU,
28 Half spaces on a plane: 4 points 28
29 Shattering: The adversarial game You An adversary You: Hypothesis class H can shatter these d points Adversary: That s what you think! Here is a labeling that will defeat you. You: Aha! There is a function h H that correctly predicts your evil labeling Adversary: Argh! You win this round. But I ll be back.. 29
30 Some functions can shatter infinite points! If arbitrarily large finite subsets of the instance space X can be shattered by a hypothesis space H. An unbiased hypothesis space H shatters the entire instance space X, i.e., it can induce every possible partition on the set of all possible instances The larger the subset of X that can be shattered, the more expressive a hypothesis space H is, i.e., the less biased it is 30
31 VapnikChervonenkis Dimension A set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples there is a function in H that gives exactly these labels to the examples Definition: The VC dimension of hypothesis space H over instance space X is the size of the largest finite subset of X that is shattered by H If there exists any subset of size d that can be shattered, VC(H) >= d Even one subset will do If no subset of size d can be shattered, then VC(H) < d 31
32 What we have managed to prove Concept class VC Dimension Half intervals 1 Intervals 2 Halfspaces in the plane 3 Why? There is a dataset of size 1 that can be shattered No dataset of size 2 can be shattered There is a dataset of size 2 that can be shattered No dataset of size 3 can be shattered There is a dataset of size 3 that can be shattered No dataset of size 4 can be shattered 32
33 More VC dimensions Concept class VC Dimension Linear threshold unit in d dimensions d 1 Neural networks Number of parameters 1 nearest neighbors infinite Intuition: A rich set of functions shatters large sets of points 33
34 Why VC dimension? Remember sample complexity Consistent learners Agnostic learning Sample complexity in both cases depends on the log of the size of the hypothesis space For infinite hypothesis spaces, its VC dimension behaves like log( H ) 34
35 VC dimension and Occam s razor Using VC(H) as a measure of expressiveness we have an Occam theorem for infinite hypothesis spaces Given a sample D with m examples, find some h H is consistent with all m examples. If Then with probability at least (1d), h has error less than e. 35
36 VC dimension and Agnostic Learning Similar statement can be made for the agnostic setting as well If we have m examples, then with probability 1!, the true error of a hypothesis h with training error err S (h) is bounded by 36
37 PAC learning: What you need to know What is PAC learning? Remember: We care about generalization error, not training error Finite hypothesis spaces Connection between size of hypothesis space and sample complexity Derive and understand the sample complexity bounds Count number of hypotheses in a hypothesis class Infinite hypotheses classes What is shattering and VC dimension? How to find VC dimension of simple concept classes? Higher VC dimensions more sample complexity 37
38 Computational Learning Theory The Theory of Generalization Probably Approximately Correct (PAC) learning Positive and negative learnability results Agnostic Learning Shattering and the VC dimension 38
39 Why computational learning theory Raises interesting theoretical questions If a concept class is weakly learnable (i.e there is a learning algorithm that can produce a classifier that does slightly better than chance), does this mean that the concept class is strongly learnable? Boosting We have seen bounds of the form true error < training error (a term with!and VC dimension) Can we use this to define a learning algorithm? Structural Risk Minimization principle Support Vector Machine 39
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