'Na ve bayes classifiers' diaporamas de présentation

Abe Gong agong@umich www-personal.umich/~agong

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Lecture 7. Basic statistical modeling

687 views • 54 slides

TEXT ANALYTICS - LABS

343 views • 34 slides

TEXT ANALYTICS - LABS

344 views • 34 slides

Learning from Imbalanced, Only Positive and Unlabeled Data

Learning from Imbalanced, Only Positive and Unlabeled Data. Yetian Chen 04-29-2009. Outline. Introduction and Problem statement 2008 UC San Diego Data Ming Competition Task 1: Supervised Learning from Imbalanced Data Sets Over-sampling and Under-sampling

436 views • 22 slides

Learning from Imbalanced, Only Positive and Unlabeled Data

Learning from Imbalanced, Only Positive and Unlabeled Data. Yetian Chen 04-29-2009. Outline. Introduction and Problem statement 2008 UC San Diego Data Ming Competition Task 1: Supervised Learning from Imbalanced Data Sets Over-sampling and Under-sampling

278 views • 22 slides

Using Support Vector Machine for Integrating Catalogs

Using Support Vector Machine for Integrating Catalogs. Ping-Tsun Chang Intelligent Systems Laboratory NTU/CSIE. Integrating Catalogs. Key Insight

227 views • 15 slides

Outline

Outline. Ensembles Co-training. Ensembles of Classifiers. Assume errors are independent Assume majority vote Prob majority is wrong=area under binomial dist. Prob 0.2 0.1. Number of classifiers in error. Enemble of 21 classifiers. If individual area is 0.3

101 views • 10 slides