2. What is it ?
● Machine Learning
● Making a program Curious !
● Teach it to decide on its own.
● Give it some intelligence
Make a program to label documents according to contents :
Sports , Technology, History, Geography, Politics etc...
3. What is it ?
Step 1.
Download a lot of documents from the web
Step 2.
Label Them !
Labeling is quite a painful task. Somehow our program should
be able to distinguish b/w the various categories.
Teach the program using examples (Training set) and make sure
it makes intelligent decision in real world situations.
Question ! How many and what examples ?
5. Example
One way : Select a few data points at random, label
them give the input output set to the program … and
let it “learn” from these examples.
Supervised Learning .
6. Example
One way : Select a few data points at random, label them give
the input output set to the program … and let it “learn” from
these examples. BUT ! Keep in mind (memory) the location of
other labeled points.
Semi Supervised & Active learning !
9. So how to make Training set smaller & smarter ??
Select the Training examples which are most uncertain … instead
of doing it at random .
The program asks Queries from the “Oracle” in the form of
unlabeled instances to be labeled.
In this way, the active learner aims to achieve high accuracy
using as few labeled instances as possible, thereby minimizing
the cost of obtaining labeled data.
Eg. Query the unlabeled point that is:
Closest to the boundary.
OR
Most Uncertain
OR
Most likely to decrease overall certainty.
Etc etc.
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. Membership Query Synthesis
The learner may request labels for any unlabeled instance in the
input space, including (and typically assuming) queries that the
learner generates de novo, rather than those sampled from some
underlying natural distribution.
BUT sometimes the queries to label are quite awkward !
*De novo means from the source,fresh & itself.
12. Stream-Based Selective Sampling
Obtain an unlabeled instance, sampled from the actual
distribution.
Now, the learner decide whether to request its label or
not.
The learner !
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. … 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. …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. 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. The Algorithm…
1.Start with a large pool of unlabeled data
Select the single most informative instance to be
labeled by the oracle
Add the labeled query to the Training set
Re-train using this newly acquired knowledge
Goto 1
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. Suggested Improvements in it ...
Dr. Burr Settles ...
The Oracle has to wait as learner “re-trains” after
each label By him/her. learner should
Ask to label a batch of queries at once instead …
Querying in BATCHES
20. Suggested Improvements in it ...
Dr. Burr Settles ...
Oracles are not always right …
They can be fatigued
Error in instruments etc
CrowdSourcing 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. 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 explicitly
Account for varying labeling costs while selecting
Queries.
eg. Kapoor et al. Proposed a decision-theoratic approach.
Takes into account both labeling & misclassification cost.
Assumption: Cost of labeling prop. To length.
22. Suggested Improvements in it ...
Dr. Burr Settles ...
If labeling cost is not known,
Try to predict the real, unknown annotation cost based on a
few simple “meta features” on the instances.
Research has shown that these learned cost-models are
significantly better than simpler cost heuristics
(e.g., a linear function of length).
23. Active Learning :: Practical Examples
Drug Design
Unlabeled Points :: A large (really large) pool of Chemical
Compounds.
Label :: Active (binds to a target) or Not.
Getting a label :: The Experiment.
25. Conclusion
Machines 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. Thank You :)
Do Check out
http://en.akinator.com
