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Web-Mining Agents: Transfer Learning TrAdaBoost

Explore the concepts and algorithms of transfer learning for supervised tasks, such as TrAdaBoost and Self-Taught Learning, to enhance target predictive functions by transferring knowledge from source domains. Understand the differences between traditional machine learning and transfer learning in terms of domain adaptation and task transfer. Delve into how to answer key questions regarding transfer learning, when to transfer, what to transfer, and how various algorithms like TrAdaBoost and Self-Taught Learning work through specific methodologies and assumptions.

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Web-Mining Agents: Transfer Learning TrAdaBoost

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  1. Web-Mining Agents:Transfer LearningTrAdaBoost R. Möller Institute of Information Systems University of Lübeck

  2. Based on an excerpt of:Transfer for Supervised Learning Tasks by: HAITHAM BOU AMMAR MAASTRICHT UNIVERSITY

  3. Traditional Machine Learning vs. Transfer Source Task Target Task Different Tasks Learning System Learning System Learning System Learning System Knowledge Traditional Machine Learning Transfer Learning

  4. Transfer Learning Definition • Notation: • Domain : • Feature Space: • Marginal Probability Distribution: • with • Given a domain then a task is : Label Space P(Y|X)

  5. 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

  6. Transfer Definition • Therefore, if either : Domain Differences Task Differences

  7. Questions to answer when transferring When to Transfer ? What to Transfer ? Model ? Features ? How to Transfer ? Instances ? Map Model ? Unify Features ? In which Situations Weight Instances ?

  8. Algorithms: TrAdaBoost • Assumptions: • Source and Target task have same feature space: • Marginal distributions are different: Not all source data might be helpful !

  9. 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

  10. Algorithm: TrAdaBoost Weights Initialization Hypothesis Learning and error calculation Weights Update

  11. Motorcycle Car Natural scenes Algorithms: Self-Taught Learning • Problem Targeted is : • Little labeled data • A lot of unlabeled data • Build a model on the labeled data

  12. Algorithms: Self-Taught Learning • Assumptions: • Source and Target task have different feature space: • Marginal distributions are different: • Label Space is different:

  13. Algorithms: Self-Taught Learning • Framework: • Source Unlabeled data set: • Target Labeled data set: Natural scenes Build classifier for cars and Motorbikes

  14. Algorithms: Self-Taught Learning • Step One: Discover high level features from Source data by Re-construction Error Regularization Term Constraint on the Bases

  15. Algorithm: Self-Taught Learning High Level Features Unlabeled Data Set

  16. Algorithm: Self-Taught Learning • Step Two: Project target data onto the attained features by • Informally, find the activations in the attained bases such that: • Re-construction is minimized • Attained vector is sparse

  17. Algorithms: Self-Taught Learning High Level Features

  18. 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)

  19. 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

  20. Next in Web-Mining Agents:Unlabeled Features Revisited Unsupervised Learning: Clustering

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