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Movie Dataset Data for 10,000 movies. Several features for each movie are given. 10,000 Text Clustering, classification 1999 [489] G. Wiederhold Open University Learning Analytics Dataset Information about students and their interactions with a virtual learning environment. None. ~ 30,000 Text Classification, clustering, regression 2015 [490] [491]
RAWPED is a dataset for detection of pedestrians in the context of railways. The dataset is labeled box-wise. 26000 Images Object recognition and classification 2020 [70] [71] Tugce Toprak, Burak Belenlioglu, Burak Aydın, Cuneyt Guzelis, M. Alper Selver OSDaR23 OSDaR23 is a multi-sensory dataset for detection of objects in the context of railways.
Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form <user, movie, date of grade, grade> . The user and movie fields are integer IDs, while grades are from 1 to 5 ( integer ) stars.
The dataset consists of around 985 million words, and the books that comprise it span a range of genres, including romance, science fiction, and fantasy. [ 3 ] The corpus was introduced in a 2015 paper by researchers from the University of Toronto and MIT titled "Aligning Books and Movies: Towards Story-like Visual Explanations by Watching ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
The full data set contains 26,000,000 ratings and 750,000 tag applications applied to 45,000 movies by 270,000 users. It also includes tag genome data with 12 million relevance scores across 1,100 tags (Last updated 8/2017). [15] There are many types of research conducted based on the MovieLens data sets.
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]
The MSU Super-Resolution for Video Compression Benchmark was organized by MSU. This benchmark tests models' ability to work with compressed videos. The dataset consists of 9 videos, compressed with different Video codec standards and different bitrates. Models are ranked by BSQ-rate [73] over subjective score. The resolution of ground-truth ...