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  2. Netflix Prize - Wikipedia

    en.wikipedia.org/wiki/Netflix_Prize

    Note that, while the actual grades are integers in the range 1 to 5, submitted predictions need not be. Netflix also identified a probe subset of 1,408,395 ratings within the training data set. The probe, quiz, and test data sets were chosen to have similar statistical properties. In summary, the data used in the Netflix Prize looks as follows:

  3. Video Multimethod Assessment Fusion - Wikipedia

    en.wikipedia.org/wiki/Video_Multimethod...

    The method has been further developed in cooperation with Netflix, using different subjective video datasets, including a Netflix-owned dataset ("NFLX"). Subsequently renamed "Video Multimethod Assessment Fusion", it was announced on the Netflix TechBlog in June 2016 [ 4 ] and version 0.3.1 of the reference implementation was made available ...

  4. Matrix completion - Wikipedia

    en.wikipedia.org/wiki/Matrix_completion

    Companies like Apple, Amazon, Barnes and Noble, and Netflix are trying to predict their user preferences from partial knowledge. In these kind of matrix completion problem, the unknown full matrix is often considered low rank because only a few factors typically contribute to an individual's tastes or preference.

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

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

    The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. High-dimensional statistics - Wikipedia

    en.wikipedia.org/wiki/High-dimensional_statistics

    This topic, which concerns the task of filling in the missing entries of a partially observed matrix, became popular owing in large part to the Netflix prize for predicting user ratings for films. High-dimensional classification. Linear discriminant analysis cannot be used when >, because the sample covariance matrix is singular.

  8. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    T-distributed Stochastic Neighbor Embedding (t-SNE) is a nonlinear dimensionality reduction technique useful for the visualization of high-dimensional datasets. It is not recommended for use in analysis such as clustering or outlier detection since it does not necessarily preserve densities or distances well. [18]

  9. Dynamic Adaptive Streaming over HTTP - Wikipedia

    en.wikipedia.org/wiki/Dynamic_Adaptive_Streaming...

    DASH is an adaptive bitrate streaming technology where a multimedia file is partitioned into one or more segments and delivered to a client using HTTP. [15] A media presentation description (MPD) describes segment information (timing, URL, media characteristics like video resolution and bit rates), and can be organized in different ways such as SegmentList, SegmentTemplate, SegmentBase and ...