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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Ling-Spam Dataset Corpus containing both legitimate and spam emails. Four version of the corpus involving whether or not a lemmatiser or stop-list was enabled. 2,412 Ham 481 Spam Text Classification 2000 [38] [39] Androutsopoulos, J. et al. SMS Spam Collection Dataset Collected SMS spam messages. None. 5,574 Text Classification 2011 [40] [41]

  3. This memory foam mattress topper has Amazon shoppers ... - AOL

    www.aol.com/lifestyle/this-memory-foam-mattress...

    This memory foam mattress topper has Amazon shoppers raving — helps with back pain and it's on sale. ... Bedluxury 3 Inch Gel Memory Foam Mattress Topper, Queen. $100 $120.

  4. Memory foam - Wikipedia

    en.wikipedia.org/wiki/Memory_foam

    A memory foam mattress is usually denser than other foam mattresses, making it both more supportive and heavier. Memory foam mattresses are often sold for higher prices than traditional mattresses. Memory foam used in mattresses is commonly manufactured in densities ranging from less than 24kg/m 3 (1.5 lb/ft 3) to 128kg/m 3 (8 lb/ft 3) density

  5. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    Kaggle was founded by Anthony Goldbloom in April 2010. [2] Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist. [3] Also on the team was Nicholas Gruen serving as the founding chair. [4] In 2011, the company raised $12.5 million and Max Levchin became the chairman. [5]

  6. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.

  7. Naive Bayes spam filtering - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_spam_filtering

    Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.