You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\nKaggle and the competition host have the right to publicly disseminate any entries or models.
\r\n\r\n Winning solutions need to be made available under\r\n a popular OSI-approved license\r\n in order to be eligible for recognition and prize money.\r\n
\r\n\r\n Prizes will be awarded after the winners have posted their solutions to the competition forum.\r\n Winners must post or link to their solutions within seven (7) days of being notified of their winning status.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 5 final submissions for judging.
\r\nStart Date: 4/12/2013 11:11 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 5/24/2013 11:59 PM UTC
\r\n\t\r\nYou must release your code under a popular OSI-approved license in order to be eligible for prize money.
\r\nThe organization will not claim any intellectual property from the winning team.
\r\nPeople affiliated with Google and with Université de Montréal may enter the contest, but no one who participated in the creation of the dataset may do so. People affiliated with Google are not eligible for prize money. People involved in the creation of the dataset are forbidden to give any advice regarding the contest to any of the competitors. In particular, no one who knows what the original data source was may enter the contest.
\r\nDo not attempt to reverse-engineer the file format in order to determine the source of the data. Solutions that can't be described without reference to knowledge of the original data format will be disqualified.
\r\nYou do not need to use a representation learning algorithm. If you think you can hand-design features for an unknown data domain fast enough to win the contest, you are encouraged to try. However, your final system must be able to autonomously classify the test data without a human in the loop.
\r\nWe will unveil the nature of the dataset after the competition ends.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "This is a black-box learning challenge: Competitors train a classifier on a dataset that is not human readable, without knowledge of what the data consists of. They are scored based on classification accuracy on a private test set. This challenge is designed to reduce the usefulness of having a human researcher working in the loop with the training algorithm.
\r\nWe are also providing a dataset of approx. 130,000 unsupervised examples that contestants can use to improve their models. The unsupervised data is a CSV file in the same format as the private test set (i.e. without the labels). The extra data comes from a distribution that is very similar to the training/test set distribution.
\r\nWe provide example code for this contest as part of the pylearn2 package at https://github.com/lisa-lab/pylearn2
\r\nFor this contest, look at the pylearn2/scripts/icml_2013_wrepl/black_box directory.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Multi-class classification accuracy will be used to judge the submissions. Note that to be scored correctly your submission file should refer to the classes as \"1.0\" through \"9.0\". If you enter a different string such as \"0.0\" or \"2\", you will not receive credit for that example.
\r\n\r\nWe have migrated the competition to the new parser. We now expect a csv file instead of a single column txt file with columns Id and Class. Id is a sequence of numbers from 1 to 10000. And we now expect classes without the decimal part \"1\" through \"9\".
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "\r\n
The data consists of 1,875 input features for each example. Each example is assigned to one of 9 classes. The training set consists of 1,000 labeled examples. The test set consists of 10,000 examples, split into 5,000 public test and 5,000 private test. The extra data download (available as a .csv file compressed into a .tgz archive) provides an additional 135,735 unlabeled examples that your training algorithm may exploit.
\r\nNote that to be scored correctly your submission file should refer to the classes as \"1.0\" through \"9.0\". If you enter a different string such as \"0.0\" or \"2\", you will receive zero points for that example.
\r\nWe have migrated the competition to the new parser. We now expect a csv file instead of a single column txt file with columns Id and Class. Id is a sequence of numbers from 1 to 10000. And we now expect classes without the decimal part \"1\" through \"9\".
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "Post-Competition Wrapup
\r\nChallenges in Representation Learning ICML 2013 Workshop
\r\nThis contest is associated with the ICML 2013 Workshop on Representation Learning.
\r\nFirst prize: $350 and an invitation to speak at the workshop.
\r\nSecond prize: $150
\r\nWe encourage submission of papers to the workshop. Scientifically interesting papers will be accepted regardless of performance in the contest.
\r\nThe funding for the prizes was generously provided by Google, Inc.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }