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Search Results for hunch

Reference vs. Referral

April 10th, 2009 · 8 Comments

I just read an interesting “guessay” by designer Joshua Porter entitled “The Slow Erosion of Google Search“–which in turn cites an insightful essay by Brynn Evans, “Why social search won’t topple Google (anytime soon)“. Brynn’s tweet alerted me to the Porter essay, which just shows that social media can have directed cycles! But what really […]

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Tags: General

Social Search at the FXPAL Blog

March 31st, 2009 · Comments Off on Social Search at the FXPAL Blog

With all of the excitement about Aardvark and Hunch, I’ve been meaning to write a big-picture post about social search. I hope you’ll forgive me, but I’ve decided to defer to the experts at FXPAL. Here are three recent posts on the subject that I recommend: Gene Golovchinsky: Social Search Gene Golovchinsky: Communicating about Collaboration: […]

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The Napoleon Dynamite Problem

November 21st, 2008 · 15 Comments

This week’s New York Times Magazine features an article by Clive Thompson about the Netflix Prize. The Netflix Prize, sponsored by the Netflix movie rental company, is perhaps the best marketing stunt I’ve seen in the history of machine learning: The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone […]

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Tags: General

Workshop on Empirical Hypothesis Spaces

October 1st, 2008 · Comments Off on Workshop on Empirical Hypothesis Spaces

Thanks to Kristiaan Pelckmans for posting about the upcoming Workshop on Empirical Hypothesis Spaces at NIPS 2008: This workshop asks for insights how far we may/can push the theoretical boundary of using data in the design of learning machines. Can we express our classification rule in terms of the sample, or do we have to […]

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Nice Selection of Machine Learning Papers

July 10th, 2008 · Comments Off on Nice Selection of Machine Learning Papers

John Langford just posted a list of seven ICML ’08 papers that he found interesting. I appreciate his taste in papers, and I particularly liked a paper on Learning Diverse Rankings with Multi-Armed Bandits that addresses learning a diverse ranking of documents based on users’ clicking behavior. If you liked the Less is More work […]

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