1. Computational Learning Theory
In the Name of God
Machine Learning
Computational Learning Theory
Mohammad Ali Keyvanrad
Thanks to:
M. Soleymani (Sharif University of Technology)
Tom Mitchell (Carnegie Mellon University )

2. Outline Computational Learning Theory PAC learning theorem
VC dimension

3. Computational Learning Theory
We want a theory that relates
Number of training examples
Complexity of hypothesis space
Accuracy to which target function is approximated
Probability that learner outputs a successful hypothesis

4. Learning scenarios Learner proposes instances as queries to teacher?
learner proposes 𝒙, teacher provides 𝑐(𝒙)
Teacher (who knows 𝑐(𝒙)) proposes training examples?
teacher proposes sequence { 𝒙 1 ,𝑐 𝒙 1 ,…, ( 𝒙 𝑛 ,𝑐 𝒙 𝑛 )}
Instances drawn according to 𝑃(𝒙)

5. Sample Complexity How good is the classifier, really?
How much data do I need to make it “good enough”?

6. Problem settings Set of all instances 𝑋 Set of hypotheses 𝐻
Set of possible target functions 𝐶={𝑐:𝑋→ 0,1 }
Sequence of 𝑚 training instances 𝐷= 𝒙 𝑖 ,𝑐 𝒙 𝑖 𝑖=1 𝑚
𝒙 drawn at random from unknown distribution 𝑃(𝒙)
Teacher provides noise-free label 𝑐(𝒙) for it
Learner observes a set of training examples 𝐷 for target function 𝑐 and outputs a hypothesis ℎ∈𝐻 estimating 𝑐
Goal: with high probability ("probably"), the selected function will have low generalization error ("approximately correct")

7. True error of a hypothesis
True error of ℎ: probability that it will misclassify an example drawn at random from 𝑃(𝒙)

8. Two notions of error

9. Overfitting Consider a hypothesis ℎ and its
Error rate over training data: 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 (ℎ)
True error rate over all data: 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 (ℎ)
We say ℎ overfits the training data if
𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 (ℎ)>𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 (ℎ)
Amount of overfitting
𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 ℎ −𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 (ℎ)
Can we bound 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 ℎ in terms of 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 ℎ ?

10. Problem setting Classification
𝐷: 𝑚 i.i.d. data points that are labeled
Finite number of possible hypothesis (e.g., decision trees of depth 𝑑 0 )
A learner finds a hypothesis ℎ that is consistent with training data
𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 (ℎ)=0
What is the probability that the true error of ℎ will be more than 𝜖?
𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 (ℎ)≥𝜖

11. How likely is a learner to pick a bad hypothesis?
Bound on the probability that any consistent learner will output ℎ with 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑢𝑒 (ℎ)>𝜖
Theorem [Haussler, 1988]: For target concept c, ∀ 0 ≤𝜖 ≤1 , If 𝐻 is finite and 𝐷 contains 𝑚≥1 independent random samples

12. Proof

13. Proof (Cont’d)

14. PAC Bound
Theorem [Haussler’88]: Consider finite hypothesis space 𝐻, training set 𝐷 with 𝑚 i.i.d. samples,0<𝜖<1: for any learned hypothesis ℎ∈𝐻 that is consistent on the training set 𝐷:
If 𝑒𝑟𝑟𝑜 𝑟 𝑡𝑟𝑎𝑖𝑛 (ℎ)=0 then
with probability at least (1−𝛿):
PAC: Probably Approximately Correct

15. PAC Bound Sample Complexity
How many training examples suffice?
Given 𝜖 and 𝛿, yields sample complexity:
Given 𝑚 and 𝛿, yields error bound:

16. Example: Conjunction of up to 𝑑 Boolean literals

17. Agnostic learning

18. Hoeffding bounds: Agnostic learning

19. PAC bound: Agnostic learning

20. Limitation of the bounds

21. Shattering a set of instances

22. Vapnick-Chervonenkis (VC) dimension

23. PAC bound using VC

24. VC dimension: linear classifier in a 2-D space

25. VC dimension: linear classifier

26. Summary of PAC bounds