DataminingConceptsandteniques9.pptVIP

* * * MK: Do we need this slide here?? We don’t give references for other topics until the reference section * * * Explore the bound in mining * One fig * * * * * * * * * * * * * * * MK: Note – different notation than used in book. Will have to standardize notation. * * * * * * * MK: Do we want to keep this slide? It is not in the text and may confuse the students. * * * * * * * * * * * * * * * * * * * * * * Transfer Learning: Conceptual Framework Transfer learning: Extract knowledge from one or more source tasks and apply the knowledge to a target task Traditional learning: Build a new classifier for each new task Transfer learning: Build new classifier by applying existing knowledge learned from source tasks * Traditional Learning Framework Transfer Learning Framework Transfer Learning: Methods and Applications Applications: Especially useful when data is outdated or distribution changes, e.g., Web document classification, e-mail spam filtering Instance-based transfer learning: Reweight some of the data from source tasks and use it to learn the target task TrAdaBoost (Transfer AdaBoost) Assume source and target data each described by the same set of attributes (features) class labels, but rather diff. distributions Require only labeling a small amount of target data Use source data in training: When a source tuple is misclassified, reduce the weight of such tupels so that they will have less effect on the subsequent classifier Research issues Negative transfer: When it performs worse than no transfer at all Heterogeneous transfer learning: Transfer knowledge from different feature space or multiple source domains Large-scale transfer learning * * Chapter 9. Classification: Advanced Methods Bayesian Belief Networks Classification by Backpropagation Support Vector Machines Classification by Using Frequent Patterns Lazy Learners (or Learning from Your Neighbors) Other Classification Methods Additional Topics Regarding Classification Summary * Summary Effective a