http://www.datameer.com It is frustrating: even with petabytes of data on a Hadoop cluster, one still encounters situations where there’s a lack of key data for a wide variety of big data analytic use cases. You might have billions of clicks on your web site, but only a few users choose to rate a product. There might be millions of text documents on your cluster, but it is too expensive to have someone categorize more than a tiny fraction of them. In principle, this is where predictive modeling could help. For instance, one could learn a model to predict user ratings so you can better serve product recommendations based on those expected ratings. Or, one could create a model to automatically categorize text documents, saving countless hours and dollars. The main problem is that there is only a limited amount of training material (i.e. user ratings, categorized documents) and it is thus hard to generate good models. As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.

1. How to do Predictive
Analytics with
Limited Data
Ulrich Rueckert
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

2. Agenda
Introduction and Motivation
Semi-Supervised Learning
• Generative Models
• Large Margin Approaches
• Similarity Based Methods
Conclusion
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

3. Predictive Modeling
Traditional Supervised
Learning
•
•
•
•
•
Promotion on bookseller’s web page
Customers can rate books.
Will a new customer like this book?
Training set: observations on previous
customers
Test set: new customers
What happens if only few
customers rate a book?
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
Test Data
Age
Attributes
Income
60K
80K
95K
52K
45K
75K
51K
+
+
+
52
47K
-
47
25
38K
22K
-
33
47K
?
+
20
43
26
41K
-
35
?
LikesBook
+
65
60
67K
39
Age
24
LikesBook
22
Target
Label
Income
+
Training Data
Model
Age
22
Income
67K
LikesBook
+
39
41K
-
Prediction

4. Predictive Modeling
Test Data
Traditional Supervised
Learning
•
•
•
•
•
Age
Test set: new customers
What happens if only few
customers rate a book?
41K
?
95K
52K
?
?
20
45K
?
43
75K
?
26
52
51K
47K
?
?
47
38K
?
25
22K
?
33
Training set: observations on previous
customers
?
60
35
Will a new customer like this book?
67K
39
Customers can rate books.
LikesBook
22
Promotion on bookseller’s web page
Income
47K
?
Target
Label
Attributes
Age
Income
LikesBook
24
60K
+
65
80K
-
Model
Training Data
Prediction
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

5. Predictive Modeling
Traditional Supervised
Learning
•
•
•
•
•
Promotion on bookseller’s web page
Customers can rate books.
Will a new customer like this book?
Training set: observations on previous
customers
Test set: new customers
What happens if only few
customers rate a book?
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
Test Data
Age
Attributes
Income
60K
80K
95K
52K
45K
75K
51K
?
+
?
52
47K
-
47
25
38K
22K
?
?
33
47K
?
?
20
43
26
41K
?
35
?
LikesBook
+
65
60
67K
39
Age
24
LikesBook
22
Target
Label
Income
?
Training Data
Model
Age
22
Income
67K
LikesBook
+
39
41K
-
Prediction

6. Supervised Learning
Classification
•
For now: each data instance is a
point in a 2D coordinate system
•
•
Color denotes target label
Model is given as decision boundary
What’s the correct model?
•
•
In theory: no way to tell
•
All learning systems have underlying
assumptions
Smoothness assumption: similar
instances have similar labels
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

7. Supervised Learning
Classification
•
For now: each data instance is a
point in a 2D coordinate system
•
•
Color denotes target label
Model is given as decision boundary
What’s the correct model?
•
•
In theory: no way to tell
•
All learning systems have underlying
assumptions
Smoothness assumption: similar
instances have similar labels
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

8. Supervised Learning
Classification
•
For now: each data instance is a
point in a 2D coordinate system
•
•
Color denotes target label
Model is given as decision boundary
What’s the correct model?
•
•
In theory: no way to tell
•
All learning systems have underlying
assumptions
Smoothness assumption: similar
instances have similar labels
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

9. Supervised Learning
Classification
•
For now: each data instance is a
point in a 2D coordinate system
•
•
Color denotes target label
Model is given as decision boundary
What’s the correct model?
•
•
In theory: no way to tell
•
All learning systems have underlying
assumptions
Smoothness assumption: similar
instances have similar labels
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

10. Supervised Learning
Classification
•
For now: each data instance is a
point in a 2D coordinate system
•
•
Color denotes target label
Model is given as decision boundary
What’s the correct model?
•
•
In theory: no way to tell
•
All learning systems have underlying
assumptions
Smoothness assumption: similar
instances have similar labels
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

11. Semi-Supervised Learning
Can we make use of the
unlabeled data?
•
•
In theory: no
... but we can make assumptions
Popular Assumptions
•
•
•
Clustering assumption
Low density assumption
Manifold assumption
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

12. Semi-Supervised Learning
Can we make use of the
unlabeled data?
•
•
In theory: no
... but we can make assumptions
Popular Assumptions
•
•
•
Clustering assumption
Low density assumption
Manifold assumption
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

13. The Clustering Assumption
Clustering
•
Partition instances into groups (clusters)
of similar instances
•
Many different algorithms: k-Means, EM,
DBSCAN, etc.
•
Available e.g. on Mahout
Clustering Assumption
•
The two classification targets are distinct
clusters
•
Simple semi-supervised learning: cluster,
then perform majority vote
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

14. The Clustering Assumption
Clustering
•
Partition instances into groups (clusters)
of similar instances
•
Many different algorithms: k-Means, EM,
DBSCAN, etc.
•
Available e.g. on Mahout
Clustering Assumption
•
The two classification targets are distinct
clusters
•
Simple semi-supervised learning: cluster,
then perform majority vote
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

15. The Clustering Assumption
Clustering
•
Partition instances into groups (clusters)
of similar instances
•
Many different algorithms: k-Means, EM,
DBSCAN, etc.
•
Available e.g. on Mahout
Clustering Assumption
•
The two classification targets are distinct
clusters
•
Simple semi-supervised learning: cluster,
then perform majority vote
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

16. The Clustering Assumption
Clustering
•
Partition instances into groups (clusters)
of similar instances
•
Many different algorithms: k-Means, EM,
DBSCAN, etc.
•
Available e.g. on Mahout
Clustering Assumption
•
The two classification targets are distinct
clusters
•
Simple semi-supervised learning: cluster,
then perform majority vote
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

17. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

18. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

19. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

20. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

21. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

22. Generative Models
Mixture of Gaussians
•
Assumption: the data in each cluster is
generated by a normal distribution
•
Find most probable location and shape
of clusters given data
Expectation-Maximization
•
•
Two step optimization procedure
•
•
Each step is one MapReduce job
Keeps estimates of cluster assignment
probabilities for each instance
Might converge to local optimum
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

23. Beyond Mixtures of Gaussians
Expectation-Maximization
•
•
Can be adjusted to all kinds of mixture models
E.g. use Naive Bayes as mixture model for text classification
Self-Training
•
•
•
•
Learn model on labeled instances only
Apply model to unlabeled instances
Learn new model on all instances
Repeat until convergence
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

24. The Low Density Assumption
Assumption
•
The area between the two classes has
low density
•
Does not assume any specific form of
cluster
Support Vector Machine
•
•
•
Decision boundary is linear
Maximizes margin to closest instances
Can be learned in one Map-Reduce step
(Stochastic Gradient Descent)
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

25. The Low Density Assumption
Assumption
•
The area between the two classes has
low density
•
Does not assume any specific form of
cluster
Support Vector Machine
•
•
•
Decision boundary is linear
Maximizes margin to closest instances
Can be learned in one Map-Reduce step
(Stochastic Gradient Descent)
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

26. The Low Density Assumption
Assumption
•
The area between the two classes has
low density
•
Does not assume any specific form of
cluster
Support Vector Machine
•
•
•
Decision boundary is linear
Maximizes margin to closest instances
Can be learned in one Map-Reduce step
(Stochastic Gradient Descent)
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

27. The Low Density Assumption
Semi-Supervised Support
Vector Machine
•
Minimize distance to labeled and
unlabeled instances
•
Parameter to fine-tune influence of
unlabeled instances
•
Additional constraint: keep class balance
correct
Implementation
•
•
Simple extension of SVM
But non-convex optimization problem
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

28. The Low Density Assumption
Semi-Supervised Support
Vector Machine
•
Minimize distance to labeled and
unlabeled instances
•
Parameter to fine-tune influence of
unlabeled instances
•
Additional constraint: keep class balance
correct
Implementation
•
•
Simple extension of SVM
But non-convex optimization problem
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

29. The Low Density Assumption
Semi-Supervised Support
Vector Machine
•
Minimize distance to labeled and
unlabeled instances
•
Parameter to fine-tune influence of
unlabeled instances
•
Additional constraint: keep class balance
correct
Implementation
•
•
Simple extension of SVM
But non-convex optimization problem
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

30. The Low Density Assumption
Semi-Supervised Support
Vector Machine
•
•
•
Minimize distance to labeled and
unlabeled instances
Parameter to fine-tune influence of
unlabeled instances
Additional constraint: keep class balance
correct
Implementation
•
•
Margins of
labeled
instances
Regularizer
2
min kwk
w
+C
+C
n
X
`(yi (wT xi + b))
i=1
n+m
X
?
`(|wT xi + b|)
i=n+1
Simple extension of SVM
But non-convex optimization problem
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
Margins of
unlabeled
instances

31. Semi-Supervised SVM
Stochastic Gradient Descent
•
•
One run over the data in random order
•
Steps get smaller over time
Each misclassified or unlabeled instance
moves classifier a bit
Implementation on Hadoop
•
Mapper: send data to reducer in random
order
•
Reducer: update linear classifier for
unlabeled or misclassified instances
•
Many random runs to find best one
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

32. Semi-Supervised SVM
Stochastic Gradient Descent
•
•
One run over the data in random order
•
Steps get smaller over time
Each misclassified or unlabeled instance
moves classifier a bit
Implementation on Hadoop
•
Mapper: send data to reducer in random
order
•
Reducer: update linear classifier for
unlabeled or misclassified instances
•
Many random runs to find best one
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

33. Semi-Supervised SVM
Stochastic Gradient Descent
•
•
One run over the data in random order
•
Steps get smaller over time
Each misclassified or unlabeled instance
moves classifier a bit
Implementation on Hadoop
•
Mapper: send data to reducer in random
order
•
Reducer: update linear classifier for
unlabeled or misclassified instances
•
Many random runs to find best one
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

34. Semi-Supervised SVM
Stochastic Gradient Descent
•
•
One run over the data in random order
•
Steps get smaller over time
Each misclassified or unlabeled instance
moves classifier a bit
Implementation on Hadoop
•
Mapper: send data to reducer in random
order
•
Reducer: update linear classifier for
unlabeled or misclassified instances
•
Many random runs to find best one
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

35. Semi-Supervised SVM
Stochastic Gradient Descent
•
•
One run over the data in random order
•
Steps get smaller over time
Each misclassified or unlabeled instance
moves classifier a bit
Implementation on Hadoop
•
Mapper: send data to reducer in random
order
•
Reducer: update linear classifier for
unlabeled or misclassified instances
•
Many random runs to find best one
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

36. The Manifold Assumption
The Assumption
•
Training data is (roughly) contained in a
low dimensional manifold
•
One can perform learning in a more
meaningful low-dimensional space
•
Avoids curse of dimensionality
Similarity Graphs
•
Idea: compute similarity scores between
instances
•
Create network where the nearest
neighbors are connected
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

37. The Manifold Assumption
The Assumption
•
Training data is (roughly) contained in a
low dimensional manifold
•
One can perform learning in a more
meaningful low-dimensional space
•
Avoids curse of dimensionality
Similarity Graphs
•
Idea: compute similarity scores between
instances
•
Create network where the nearest
neighbors are connected
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

38. The Manifold Assumption
The Assumption
•
Training data is (roughly) contained in a
low dimensional manifold
•
One can perform learning in a more
meaningful low-dimensional space
•
Avoids curse of dimensionality
Similarity Graphs
•
Idea: compute similarity scores between
instances
•
Create network where the nearest
neighbors are connected
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

39. The Manifold Assumption
The Assumption
•
Training data is (roughly) contained in a
low dimensional manifold
•
One can perform learning in a more
meaningful low-dimensional space
•
Avoids curse of dimensionality
Similarity Graphs
•
Idea: compute similarity scores between
instances
•
Create network where the nearest
neighbors are connected
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

40. Label Propagation
Main Idea
•
Propagate label information to
neighboring instances
•
•
Then repeat until convergence
Similar to PageRank
Theory
•
Known to converge under weak
conditions
•
Equivalent to matrix inversion
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

41. Label Propagation
Main Idea
•
Propagate label information to
neighboring instances
•
•
Then repeat until convergence
Similar to PageRank
Theory
•
Known to converge under weak
conditions
•
Equivalent to matrix inversion
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

42. Label Propagation
Main Idea
•
Propagate label information to
neighboring instances
•
•
Then repeat until convergence
Similar to PageRank
Theory
•
Known to converge under weak
conditions
•
Equivalent to matrix inversion
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

43. Label Propagation
Main Idea
•
Propagate label information to
neighboring instances
•
•
Then repeat until convergence
Similar to PageRank
Theory
•
Known to converge under weak
conditions
•
Equivalent to matrix inversion
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

44. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Reducers
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
1
1 1
2
1
3
Smarter Approaches
•
Data
1
4
1
1
1
1

45. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Reducers
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
1
1 1
1 2
1
1
2
2 1
2 2
2
2
3
Smarter Approaches
•
Data
1
2
4
1
2

46. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Reducers
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
1
1 1
1 2
1 3
1 4
2
2 1
2 2
2 3
2 4
3
Smarter Approaches
•
Data
3 1
3 2
3 3
3 4
4
4 1
4 2
4 3
4 4

47. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Smarter Approaches
•
Data
1
2
3
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
4
Nearest Neighbors

48. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Smarter Approaches
•
Data
1
2
3
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
4
Nearest Neighbors
Result

49. Nearest Neighbor Join
Block Nested Loop Join
•
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
2nd MR job: filter out the overall nearest
neighbors
Smarter Approaches
•
Data
1
1 1
1 2
2
2 1
2 2
2 3
3 2
3 3
3 4
4 3
4 4
3
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13
Reducers
4

50. Nearest Neighbor Join
Block Nested Loop Join
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
•
2nd MR job: filter out the overall nearest
neighbors
Smarter Approaches
•
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

51. Nearest Neighbor Join
Block Nested Loop Join
•
1st MR job: partition data into blocks,
compute nearest neighbors between
blocks
•
2nd MR job: filter out the overall nearest
neighbors
Smarter Approaches
•
Use spatial information to avoid
unnecessary comparisons
•
R trees, space filling curves, locality
sensitive hashing
•
Example implementations available online
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

52. Label Propagation on Hadoop
Iterative Procedure
•
•
Mapper: Emit neighbor-label pairs
•
Repeat until convergence
Reducer: Collect incoming labels per
node and combine them into new label
Improvement
•
Cluster data and perform within-cluster
propagation locally
•
Sometimes it’s more efficient to perform
matrix inversion instead
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

53. Label Propagation on Hadoop
Iterative Procedure
•
•
Mapper: Emit neighbor-label pairs
•
Repeat until convergence
Reducer: Collect incoming labels per
node and combine them into new label
Improvement
•
Cluster data and perform within-cluster
propagation locally
•
Sometimes it’s more efficient to perform
matrix inversion instead
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

54. Label Propagation on Hadoop
Iterative Procedure
•
•
Mapper: Emit neighbor-label pairs
•
Repeat until convergence
Reducer: Collect incoming labels per
node and combine them into new label
Improvement
•
Cluster data and perform within-cluster
propagation locally
•
Sometimes it’s more efficient to perform
matrix inversion instead
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

55. Label Propagation on Hadoop
Iterative Procedure
•
•
Mapper: Emit neighbor-label pairs
•
Repeat until convergence
Reducer: Collect incoming labels per
node and combine them into new label
Improvement
•
Cluster data and perform within-cluster
propagation locally
•
Sometimes it’s more efficient to perform
matrix inversion instead
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

56. Label Propagation on Hadoop
Iterative Procedure
•
•
Mapper: Emit neighbor-label pairs
•
Repeat until convergence
Reducer: Collect incoming labels per
node and combine them into new label
Improvement
•
Cluster data and perform within-cluster
propagation locally
•
Sometimes it’s more efficient to perform
matrix inversion instead
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

57. Conclusion
Semi-Supervised Learning
•
Only few training instances have labels
•
Unlabeled instances can still provide valuable signal
Different assumptions lead to different approaches
•
•
•
Cluster assumption: generative models
Low density assumption: semi-supervised support vector machines
Manifold assumption: label propagation
Code available: https://github.com/Datameer-Inc/lesel
© 2013 Datameer, Inc. All rights reserved.
Thursday, October 31, 13

58. @Datameer
Thursday, October 31, 13