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Sathiya Keerthi's Homepage

I am a researcher in Criteo Research. A great team of researchers (spread out in Paris, Grenoble and Palo Alto) working on fundamental and applied research problems in computational advertising, with Suju Rajan. As the VP Head of Research. Previously I was in Microsoft (June 2012-December 2017) (located in Mountain View, CA), first with the CISL team in Big Data and later with the FAST division of Microsoft Office. From January 2004-April 2012 I was with the Machine Learning Group of Yahoo! In 2010 I att...

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Sathiya Keerthi's Homepage | keerthis.com Reviews

<META> DESCRIPTION

I am a researcher in Criteo Research. A great team of researchers (spread out in Paris, Grenoble and Palo Alto) working on fundamental and applied research problems in computational advertising, with Suju Rajan. As the VP Head of Research. Previously I was in Microsoft (June 2012-December 2017) (located in Mountain View, CA), first with the CISL team in Big Data and later with the FAST division of Microsoft Office. From January 2004-April 2012 I was with the Machine Learning Group of Yahoo! In 2010 I att...

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1 s sathiya keerthi

2 research

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4 liblinear

5 publications

6 abstract

7 with tobias schnabel

8 and rajiv khanna

9 the pascal challenge

10 semi supervised gaussian processes

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Sathiya Keerthi's Homepage | keerthis.com Reviews

https://keerthis.com

I am a researcher in Criteo Research. A great team of researchers (spread out in Paris, Grenoble and Palo Alto) working on fundamental and applied research problems in computational advertising, with Suju Rajan. As the VP Head of Research. Previously I was in Microsoft (June 2012-December 2017) (located in Mountain View, CA), first with the CISL team in Big Data and later with the FAST division of Microsoft Office. From January 2004-April 2012 I was with the Machine Learning Group of Yahoo! In 2010 I att...

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keerthis.com

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Comparison of measures for tuning SVM Hyperparameters

http://www.keerthis.com/comparison.html

Evaluation of simple performance measures for tuning SVM hyperparameters. K Duan, S.S. Keerthi. And AN. Poo. Dept of Mechanical Engineering. National University of Singapore. Using the feedback received from several researchers, we revised the technical report on Sept. 12, 2001. The pdf file given below contains the revised version. Pdf file containing Technical Report (revised on Sept. 12, 2001). Pdf file containing figures not included in the Report (revised on Sept. 12, 2001). The file, COMF18.TRA.

2

Procedure for using the matlab interface for the NPA code.

http://www.keerthis.com/nparm.html

Efficient Tuning of SVM Hyperparameters Using Radius/Margin Bound and Iterative Algorithms. For a gzipped postscript file containing the Tech Report CD-01-02. Procedure to be followed for using the NPA code:. You need Matlab5.3 running on Windows to run the software. Download the following files:. The dll works only on Matlab 5.3. I haven't had the time to sit down and get new dll's for future matlab versions. :(. Do the following on your machine. Copy npa.m to the C: MATLABR11 toolbox npa directory.

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vikas.sindhwani.org

svmlin: Fast Linear SVM Solvers for Supervised andSemi-supervised Learning

http://www.vikas.sindhwani.org/svmlin.html

Fast Linear SVM Solvers for Supervised and Semi-supervised Learning. Department of Computer Science. Is software package for linear SVMs. It is well-suited to classification problems involving a large number of examples and features. It is primarily written for sparse datasets (number of non-zero features in an example is typically small). It is written in C (mostly C). A mex wrapper is available for MATLAB users. Implements the following algorithms (See references [1]. The current release [v1.0 - Au...

blog.cloudera.com

The New Wrangle Conference: Solving the Hardest Data Science Challenges from Startup to Enterprise - Cloudera Engineering Blog

http://blog.cloudera.com/blog/2015/07/the-new-wrangle-conference-solving-the-hardest-data-science-challenges-from-startup-to-enterprise

Skip to main content. Best practices, how-tos, use cases, and internals from Cloudera Engineering and the community. The New Wrangle Conference: Solving the Hardest Data Science Challenges from Startup to Enterprise. July 20, 2015. Wrangle, a new conference dedicated to the practice of data science from startup to enterprise, debuts in San Francisco on Oct. 22, 2015. So, Cloudera is pleased to announce that on Oct. 22 in San Francisco, it is organizing and hosting the first-ever Wrangle Conference. 2 res...

opt-ml.org

OPT 2010, 3rd International Workshop on Optimization for Machine Learning (10.12.2010)

http://www.opt-ml.org/oldopt/opt10

OPT 2010: 3rd International Workshop on Optimization for Machine Learning. 10th December, 2010 - - NIPS*2010 WORKSHOP. Welcome to OPT 2010, the 3rd International Workshop on Optimization for Machine Learning. Held as a part of the NIPS workshops series. This workshop builds on precedent established by two of our previously very well-received NIPS workshops:. NIPS*08): http:/ opt2008.kyb.tuebingen.mpg.de/. NIPS*09): http:/ opt.kyb.tuebingen.mpg.de/opt09/. We also do NOT. Setting, where one has time to wie...

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keerthis.com

Sathiya Keerthi's Homepage

I am a researcher in Criteo Research. A great team of researchers (spread out in Paris, Grenoble and Palo Alto) working on fundamental and applied research problems in computational advertising, with Suju Rajan. As the VP Head of Research. Previously I was in Microsoft (June 2012-December 2017) (located in Mountain View, CA), first with the CISL team in Big Data and later with the FAST division of Microsoft Office. From January 2004-April 2012 I was with the Machine Learning Group of Yahoo! In 2010 I att...

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