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Pattern Discovery in Sequences under a Markov assumption Darya Chudova, Padhraic Smyth

Pattern Discovery in Sequences under a Markov assumption Darya Chudova, Padhraic Smyth

Pattern Discovery in Sequences under a Markov assumption Darya Chudova, Padhraic Smyth Information and Computer Science University of California, Irvine Funding Acknowledgements National Science Foundation, Microsoft Research, IBM Research. Outline. ?. Pattern discovery problem

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782 views • 67 slides
Lecture 3 Nonparametric density estimation and classification

Lecture 3 Nonparametric density estimation and classification

Lecture 3 Nonparametric density estimation and classification. Density estimation Histogram The box kernel -- Parzen window K-nearest neighbor. Density estimation. Classification can be based on estimating the density for each of the classes. From a set of observed random vectors,

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326 views • 32 slides
Nonparametric density estimation and classification

Nonparametric density estimation and classification

Nonparametric density estimation and classification. Density estimation Histogram The box kernel -- Parzen window K-nearest neighbor. Density estimation. Classification can be based on estimating the density for each of the classes. From a set of observed random vectors,

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325 views • 32 slides
A decision-theoretic view of image retrieval

A decision-theoretic view of image retrieval

A decision-theoretic view of image retrieval. Nuno Vasconcelos Compaq Computer Corporation Cambridge Research Lab http://www.media.mit.edu/~nuno. horses. Texture similarity. Color similarity. Shape similarity. Content-based retrieval.

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421 views • 28 slides
Midterm Review

Midterm Review

Midterm Review. 1-Intro. Data Mining vs. Statistics Predictive v. experimental; hypotheses vs data-driven Different types of data Data Mining pitfalls With lots of data you can find anything Data privacy and security Good and bad examples. 2- EDA and Visualization.

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169 views • 8 slides
k-Nearest Neighbourhood

k-Nearest Neighbourhood

k-Nearest Neighbourhood . k - Nearest Neighbor. ?. Requires 3 things: The set of stored patterns Distance metric to compute distance between patterns The value of k , the number of nearest neighbors to retrieve To classify an unknown record: Compute distance to other training patterns

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276 views • 7 slides

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