The progress prize winners have now updated their papers to respond to the judges' comments. The papers are available here. Many thanks to the prize winners for their hard work, to the judges for their thoughtful reviews, and to all competitors who assisted in reviewing the papers.

We have already seen the top 50 placeholders in the competition improve dramatically since the release of the original papers - it's great to see how the progress prize winners ideas are being utilised by other Kagglers.

Hi Jeremy,

The top 50 have actually been quite static - it is everyone else who is catching up...

http://anotherdataminingblog.blogspot.com/2011/11/pack-is-catching-up.html

Great graph, really tells the story.

Thanks !

I'm pleased to announce that the progress prize winners have now been finalized! Congratulations to Market Makers and Willem Mestrom.

Hurray !

Send out their checks ASAP.

And four complementary, Kaggle-branded, "I beat the guy who beat Watson *" trophy shirts.

(In very small print they should also say "* but Raimondi's still in the game, and it ain't over til it's over.")

Just a suggestion.

The url link pointed from Market Makers paper on creating an ensemble solution out of many models with R is not working (forum post removed). Could you please update it:

http://ausdm09.freeforums.org/improving-generalisation-by-ensembling-t13.html

Thanks for pointing this out. This was a free forum site that was used used for a similar kaggle style competiton back in 2009. Yesterday they decided to remove the forum as it has not been used for a while. I've emailed the organisers to ask them to put it back.

All the detail about the competiton can be found in the following, although unfortunately not the code examples our paper refers to. See the baseline entries section for details of what was being referred to (although ridge regression would be another alternative), 

http://www.tiberius.biz/ausdm09/AusDM09EnsemblingChallenge.pdf 

Any luck in getting the forum back online?

Thanks for your code so far. Great learning tools!

sorry - I tried but it has gove for good :-(

Ah well, us newbies will just have to work harder ;-)

@afirepebble

If you want to learn the basics of R model ensembling, you may like to check out this tutorial with R code:
http://viksalgorithms.blogspot.jp/2012/01/intro-to-ensemble-learning-in-r.html