Web-Mining Agents: Transfer Learning TrAdaBoost R. Möller Institute of Information Systems University of Lübeck
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Web-Mining Agents: Transfer Learning
TrAdaBoost
R. Möller Institute of Information Systems
University of Lübeck
b y : H A I T H A M B O U A M M A R
M A A S T R I C H T U N I V E R S I T Y
Based on an excerpt of: Transfer for Supervised
Learning Tasks
Traditional Machine Learning vs. Transfer
Source Task
Knowledge
Target Task
Learning System
Different Tasks
Learning System
Learning System
Learning System
Traditional Machine Learning Transfer Learning
Transfer Learning Definition
� Notation: ¡ Domain :
÷ Feature Space: ÷ Marginal Probability Distribution:
¢ with
¡ Given a domain then a task is :
DX
P (X)X = {x1, . . . , xn} � X
T = {Y, f(.)}
Transfer Learning Definition
Given a source domain and source learning task, a target domain and a target learning task, transfer learning aims to help improve the learning of the target predictive function using the source knowledge, where
or Ds �= DT Ts �= TT
Transfer Definition
� Therefore, if either :
XS �= XT PS(X) �= PT (X)
YS �= YT P (YS |XS) �= P (YT |XT )
Domain Differences
Task Differences
Questions to answer when transferring
Algorithms: TrAdaBoost
� Assumptions: ¡ Source and Target task have same feature space:
¡ Marginal distributions are different: XS = XT
PS(X) 6= PT (X)
Not all source data might be helpful !
Algorithm: TrAdaBoost
� Idea: ¡ Iteratively reweight source samples such that:
÷ reduce effect of “bad” source instances ÷ encourage effect of “good” source instances
� Requires: ¡ Source task labeled data set ¡ Very small Target task labeled data set ¡ Unlabeled Target data set ¡ Base Learner
Algorithm: TrAdaBoost
Weights Initialization
Hypothesis Learning and error calculation
Weights Update
Algorithms: Self-Taught Learning
Algorithms: Self-Taught Learning
� Assumptions: ¡ Source and Target task have different feature space:
¡ Marginal distributions are different:
¡ Label Space is different:
PS(X) 6= PT (X)
YS �= YT
XS �= XT
Algorithms: Self-Taught Learning
� Framework: ¡ Source Unlabeled data set:
¡ Target Labeled data set:
DS = {(x(i)s )}mi=1
DT = {(x(j)T , y
(j)T )}nj=1 with n <<< m
Build classifier for cars and Motorbikes
Algorithms: Self-Taught Learning
� Step One: Discover high level features from Source data by
Regularization Term Re-construction Error
Constraint on the Bases
minb,a
mX
i=1
||x(i)s �
X
k
a(k)si bk||22 + �||asi ||1
s.t. ||bk|| ⇥ 1, ⌅k ⇤ {1, . . . ,Ks}
Algorithm: Self-Taught Learning
Unlabeled Data Set
minb,a
mXi=1
||x(i)s�X
k
a(k)s ib k||
22+�||a
s i|| 1
s.t. ||b
k|| ⇥
1,⌅k
⇤ {1, .
. . ,K s}
Algorithm: Self-Taught Learning
� Step Two: Project target data onto the attained features by
a�Tj= argmin
aTj
||xTj �X
k
a(k)Tjbk||22 + �||aTj ||1
Informally, find the activations in the attained bases such that: 1. Re-construction is minimized 2. Attained vector is sparse
Algorithms: Self-Taught Learning
a �Tj = argmina
Tj ||xT
j � Xk a (k)T
j bk || 22 + �||aT
j ||1
Algorithms: Self-Taught Learning
� Step Three: Learn a Classifier with the new features
Target Task
Source Task
Learn new features (Step 1)
Project target data (Step 2)
Learn Model (Step 3)
Conclusions
� Transfer learning is to re-use source knowledge to help a target learner
� Transfer learning is not generalization
� TrAdaBoost transfers instances
� Self-Taught Learning transfers unlabeled features
Next in Web-Mining Agents:
Unlabeled Features Revisited Unsupervised Learning: Clustering
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