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  2. H&M - Wikipedia

    en.wikipedia.org/wiki/H&M

    H & M Hennes & Mauritz AB, commonly known by its brand name H&M, is a Swedish multinational fashion retailer headquartered in Stockholm. Known for its fast fashion business model, H&M sells clothing, accessories, and homeware. The company has a significant global presence, operating thousands of stores across 75 geographical markets and ...

  3. KMD Brands - Wikipedia

    en.wikipedia.org/wiki/KMD_Brands

    KMD Brands, formerly Kathmandu Holdings, is a global outdoor, lifestyle and sports company consisting of three brands: Kathmandu, Rip Curl and Oboz.Kathmandu was founded in 1987 in New Zealand and specialises in clothing and equipment for travel and the outdoors.

  4. Northland Shopping Centre - Wikipedia

    en.wikipedia.org/wiki/Northland_Shopping_Centre

    The centre is anchored by a Myer department store (4 levels), Target and Kmart department stores, Coles, Woolworths and Aldi supermarkets, as well as mini majors Best & Less, JB Hi-Fi, Rebel Sport, TK Maxx and Chemist Warehouse, and international retailers H&M, Uniqlo and Sephora.

  5. File:H&M-Logo.svg - Wikipedia

    en.wikipedia.org/wiki/File:H&M-Logo.svg

    The following other wikis use this file: Usage on af.wikipedia.org H&M; Usage on ar.wikipedia.org إتش أند أم; Usage on arz.wikipedia.org اتش اند ام

  6. Henry Molaison - Wikipedia

    en.wikipedia.org/wiki/Henry_Molaison

    Henry Gustav Molaison (February 26, 1926 – December 2, 2008), known widely as H.M., was an American who had a bilateral medial temporal lobectomy to surgically resect the anterior two thirds of his hippocampi, parahippocampal cortices, entorhinal cortices, piriform cortices, and amygdalae in an attempt to cure his epilepsy.

  7. Hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_model

    Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]

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