Machine Learning

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Machine Learning

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Machine Learning

  1. 1. Active LearningShrey Malik0901CS32s.malik@iitp.ac.in
  2. 2. What is it ?● Machine Learning ● Making a program Curious ! ● Teach it to decide on its own. ● Give it some intelligenceMake a program to label documents according to contents :Sports , Technology, History, Geography, Politics etc...
  3. 3. What is it ?Step 1. Download a lot of documents from the webStep 2. Label Them !Labeling is quite a painful task. Somehow our program shouldbe able to distinguish b/w the various categories.Teach the program using examples (Training set) and make sureit makes intelligent decision in real world situations.Question ! How many and what examples ?
  4. 4. Example All kinds of “unlabeled” data
  5. 5. ExampleOne way : Select a few data points at random, labelthem give the input output set to the program … andlet it “learn” from these examples.Supervised Learning .
  6. 6. ExampleOne way : Select a few data points at random, label them givethe input output set to the program … and let it “learn” fromthese examples. BUT ! Keep in mind (memory) the location ofother labeled points.Semi Supervised & Active learning !
  7. 7. ExampleGot a better generalization this time !Didnt we ??
  8. 8. Active LearningSomehow make the set of training examples smaller& results, more accurate.
  9. 9. So how to make Training set smaller & smarter ??Select the Training examples which are most uncertain … insteadof doing it at random .The program asks Queries from the “Oracle” in the form ofunlabeled instances to be labeled.In this way, the active learner aims to achieve high accuracyusing as few labeled instances as possible, thereby minimizingthe cost of obtaining labeled data.Eg. Query the unlabeled point that is:Closest to the boundary.OR Most UncertainORMost likely to decrease overall certainty.Etc etc.
  10. 10. How does the learner ask queries ?There are several different problem scenarios in which the learner may be able to ask queries. For example:
  11. 11. Membership Query Synthesis The learner may request labels for any unlabeled instance in theinput space, including (and typically assuming) queries that thelearner generates de novo, rather than those sampled from someunderlying natural distribution. BUT sometimes the queries to label are quite awkward ! *De novo means from the source,fresh & itself.
  12. 12. Stream-Based Selective Sampling Obtain an unlabeled instance, sampled from the actualdistribution. Now, the learner decide whether to request its label ornot. The learner !
  13. 13. Pool-Based Sampling For many real-world learning problems, large collections of unlabeled data Can be gathered at once. The learner ! Se le cts t he Be st Qu er y A large pool of Instances
  14. 14. … and how does the program select the best Query ??Uncertainty Sampling Query the instance for which it is least confident. x∗ = max( 1 − P ( y | x ) ) Where y = max( P ( y | x ) ) X* = The best Query P(y|x) = conditional probability …
  15. 15. …and how does the program select the best Query ?? Query-By-Committee Maintain a committee of models all trained on `that` Input space & let them label it … Now select the queries for which they disagree the most !
  16. 16. For measuring the level of disagreement: Yi :: ranges over all possible labelings. V (yi ) :: number of “votes” that a label receives from among the committee members’ predictions. C :: Committee size ! … and then there are a lot of other algorithms also !
  17. 17. The Algorithm…1.Start with a large pool of unlabeled dataSelect the single most informative instance to belabeled by the oracleAdd the labeled query to the Training setRe-train using this newly acquired knowledgeGoto 1
  18. 18. Is Active learning The thing ?Assumptions 1.Annotator, the Oracle is always right . 2.If Annotator is wrong, see rule one ! 3.Labeling is sooo expansive … is it ??? So can my machine learn more economically if it is allowed to ask questions ??? Are you from delhi ? Used the metro ? Seen the qutub minar ?
  19. 19. Suggested Improvements in it ...Dr. Burr Settles ...The Oracle has to wait as learner “re-trains” aftereach label By him/her. learner shouldAsk to label a batch of queries at once instead …Querying in BATCHES
  20. 20. Suggested Improvements in it ...Dr. Burr Settles ...Oracles are not always right … They can be fatigued Error in instruments etcCrowdSourcing on web You just played a fun game : Tag as many rockstars in the pic as you can in one minute Challenge your friends Like on facebook ...meanwhile the learner was learning from your labels … thanku Oracle !
  21. 21. Suggested Improvements in it ...Dr. Burr Settles ...Goal: to minimize the overall of training an accurate model. Simply reducing the number of labeled instances Wont help.Cost Sensitive Active Learning approaches explicitlyAccount for varying labeling costs while selectingQueries.eg. Kapoor et al. Proposed a decision-theoratic approach. Takes into account both labeling & misclassification cost. Assumption: Cost of labeling prop. To length.
  22. 22. Suggested Improvements in it ...Dr. Burr Settles ...If labeling cost is not known,Try to predict the real, unknown annotation cost based on afew simple “meta features” on the instances.Research has shown that these learned cost-models aresignificantly better than simpler cost heuristics(e.g., a linear function of length).
  23. 23. Active Learning :: Practical Examples Drug DesignUnlabeled Points :: A large (really large) pool of ChemicalCompounds.Label :: Active (binds to a target) or Not.Getting a label :: The Experiment.
  24. 24. Active Learning :: Practical Examples Pedestrian Detection
  25. 25. ConclusionMachines should be able to do all the things we hate … &machine learning will play a big role in achieving this goal.And to make machine learning faster and cheaper … active learning is the key !Machine/Active learning is a very good area for research ! Machines will become Intelligent and wage a war against Humanity !
  26. 26. Thank You :) Do Check outhttp://en.akinator.com

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