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Joseph K. Bradley

Joseph K. Bradley

Sample Complexity of CRF Parameter Learning. Joseph K. Bradley. Joint work with Carlos Guestrin CMU Machine Learning Lunch talk on work appearing in AISTATS 2012. 4 / 9 / 2012. Markov Random Fields (MRFs). Goal: Model distribution P(X) over random variables X. E.g.,.

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626 views • 50 slides
Evaluation Metrics CS229

Evaluation Metrics CS229

Evaluation Metrics CS229. Anand Avati. Topics. Why? Binary classifiers Rank view , Thresholding Metrics Confusion Matrix Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity, F-score Summary metrics: AU-ROC, AU-PRC, Log-loss. Choosing Metrics Class Imbalance

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338 views • 31 slides
Learning From String Sequences

Learning From String Sequences

Learning From String Sequences. David Lindsay Supervisors: Zhiyuan Luo, Alex Gammerman and Volodya Vovk. Overview. Background: Pattern Recognition of String Data Traditional Approaches: String to Word Vector (SWV) & Implementation Issues Learning with K -Nearest Neighbours ( K -NN)

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247 views • 16 slides
Active learning Query Strategies

Active learning Query Strategies

Active learning Query Strategies. September 27 2010. Outline. Previous lecture: Uncertainty Sampling Density Weighted Methods This lecture: Query-By-Committee Expected Model Change Expected Error Reduction Variance Reduction . Query-By-Committee.

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603 views • 16 slides
Sample Complexity of Composite Likelihood

Sample Complexity of Composite Likelihood

Sample Complexity of Composite Likelihood. Joseph K. Bradley & Carlos Guestrin. better. better. better. PAC-learning parameters for general MRFs & CRFs via practical methods: pseudolikelihood & structured composite likelihood.

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75 views • 1 slides

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