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

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

    Covertype Dataset Data for predicting forest cover type strictly from cartographic variables. Many geographical features given. 581,012 Text Classification 1998 [310] [311] J. Blackard et al. Abscisic Acid Signaling Network Dataset Data for a plant signaling network. Goal is to determine set of rules that governs the network. None. 300 Text

  3. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    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.

  4. Data preprocessing - Wikipedia

    en.wikipedia.org/wiki/Data_Preprocessing

    Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...

  5. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    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. 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.

  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. The Pile (dataset) - Wikipedia

    en.wikipedia.org/wiki/The_Pile_(dataset)

    The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]

  8. CIFAR-10 - Wikipedia

    en.wikipedia.org/wiki/CIFAR-10

    The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]

  9. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.