'Bayesian classifiers' diaporamas de présentation

Information Retrieval Search Engine Technology (5&6) tangra.si.umich/clair/ir09

Information Retrieval Search Engine Technology (5&6) http://tangra.si.umich.edu/clair/ir09. Prof. Dragomir R. Radev radev@umich.edu. Final projects. Two formats:

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Weka

Weka. Preprocessing. Opening a file. Editing a file. Visualize a variable. Visualizing pairs of variables. Missing values. Discretizing with equalfrequency. Proportional k-interval discretization. FSS: ranking variables with mutual information. Filter FSS: CFS.

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Data Mining - CSE5230

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Bayesian Online Classifiers for Text Classification and Filtering

Bayesian Online Classifiers for Text Classification and Filtering. A Paper by Kian Ming Adam Chai, Hwee Tou Ng and Hai Leong Chieu Presented by Eric Franklin and Changshu Jian. Agenda. Problem Description & Proposed Solution Algorithm Description Evaluation & Conclusions

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Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille

Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille. Presented by: Hong Lu. Key Questions. Can low cost wearable sensors be used for robust, real- time recognition of activity?

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Pattern Recognition

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Classification Bayesian Classifiers

Classification Bayesian Classifiers. Bayesian classification. A probabilistic framework for solving classification problems. Used where class assignment is not deterministic, i.e. a particular set of attribute values will sometimes be associated with one class, sometimes with another.

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Classification Bayesian Classifiers

Classification Bayesian Classifiers. Bayesian classification. A probabilistic framework for solving classification problems. Used where class assignment is not deterministic, i.e. a particular set of attribute values will sometimes be associated with one class, sometimes with another.

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Dear SIR,

Dear SIR,

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CSSE463: Image Recognition Day 31

CSSE463: Image Recognition Day 31. Today: Bayesian classifiers Tomorrow: Out-of-class project workday Thursday: quiz Friday: next milestone meeting Questions?. Bayesian classifiers. Use training data Assume that you know probabilities of each feature. If 2 classes:

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CSSE463: Image Recognition Day 31

CSSE463: Image Recognition Day 31. Today: Bayesian classifiers Questions?. Bayesian classifiers. Use training data Calculate the probabilities of each feature. If 2 classes: Classes w 1 and w 2 Say, circles vs. non-circles A single feature, x Both classes equally likely

134 views • 13 slides