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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Mahout Usage and Beyond
Date Fri, 12 Feb 2010 22:49:09 GMT
On Fri, Feb 12, 2010 at 4:27 AM, Robin Anil <robin.anil@gmail.com> wrote:

>   1. Locally Weighted Linear Regression
>

Not sure how important this one is.


>    2. Naive Bayes(We have this and CBayes as a bonus)
>   3. Gaussian Discriminative Analysis (GDA)
>

DP clustering does this, effectively, I think.


>    4. Logistic Regression (LR) (In development)
>

SGD.  In dev as you say.


>    5. k-means(we have this and kmeans++ is in development)
>   6. Neural Network (NN)
>

SGD could implement this if we like.  Not sure that we need M/R to get speed
here.


>    7. Principal Components Analysis (PCA)
>

= SVD and Jake's contribution.


>    8. Independent Component Analysis (ICA)
>   9. Expectation Maximization (EM) (We have it in pig script and in couple
>   of algorithms not generic yet)
>

DP clustering is a version of this for some applications.


>    10. Support Vector Machine (SVM)(In development - The pegasus version)
>


So I think that we are actually at about 7 or 8 / 10 with several
interesting additions.

More than the original 10, we need realistic and simple examples.

-- 
Ted Dunning, CTO
DeepDyve

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