## Semi-Supervised Learning and Learning via Similarity Functions: two key settings for Data-dependent Concept Spaces

author: Avrim Blum, School of Computer Science, Carnegie Mellon University
published: Dec. 20, 2008,   recorded: December 2008,   views: 536
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# Slides

0:00 Slides Semi-Supervised Learning and Learning via Similarity Functions: Two key settings for Data-Dependent Concept Spaces Theme of the workshop: Data-Dependent Concept Spaces - 1 Theme of the workshop: Data-Dependent Concept Spaces - 2 Semi-Supervised Learning - 1 Semi-Supervised Learning - 2 Ex 1: Co-Training - 1 Ex 1: Co-Training - 2 Ex 1: Co-Training - 3 Simple Example: Intervals Ex 1: Co-Training - 4 Ex 1: Co-Training - 5 Ex 2: S3VM [Joachims98] - 1 Ex 2: S3VM [Joachims98] - 2 Ex 3: Graph-Based Methods - 1 Ex 3: Graph-Based Methods - 2 Ex 3: Graph-Based Methods - 3 Ex 3: Graph-Based Methods - 4 Several Different Approaches Semi-Supervised Model - 1 Semi-Supervised Model - 2 Semi-Supervised Model - 3 Formally - 1 Formally - 2 Can Use to Prove Sample Bounds - 1 Can Use to Prove Sample Bounds - 2 Can Use to Prove Sample Bounds - 3 Can Use to Prove Sample Bounds - 4 Examples - Questions Conclusions part 1 Topic 2: Learning with Similarity Functions Quick Reminder on Kernels Moreover, Generalize Well if Good Margin - 1 Moreover, Generalize Well if Good Margin - 2 Can We Define a Notion of a Good Measure of Similarity that… Warmup Property Just Do Average-Nearest-Nbr But not Broad Enough - 1 But not Broad Enough - 2 Broader Defn… - 1 Broader Defn… - 2 How to Use such a Sim Fn? - 1 How to Use such a Sim Fn? - 2

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