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CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [18] [36] A. Krizhevsky et al. CINIC-10 Dataset A unified contribution of CIFAR-10 and Imagenet with 10 classes, and 3 splits. Larger than CIFAR-10.
Images may appear in more than one class. The dataset was motivated by non-parametric models of neural activations in the visual cortex upon seeing images. [1] [2] The CIFAR-10 dataset uses a subset of the images in this dataset, but with independently generated labels, as the original labels were not reliable. The CIFAR-10 set has 6000 ...
Vicon Physical Action Data Set Dataset 10 normal and 10 aggressive physical actions that measure the human activity tracked by a 3D tracker. Many parameters recorded by 3D tracker. 3000 Text Classification 2011 [170] [171] T. Theodoridis Daily and Sports Activities Dataset Motor sensor data for 19 daily and sports activities.
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]
Thus, a representation that compresses the storage size of a file from 10 MB to 2 MB yields a space saving of 1 - 2/10 = 0.8, often notated as a percentage, 80%. For signals of indefinite size, such as streaming audio and video, the compression ratio is defined in terms of uncompressed and compressed data rates instead of data sizes:
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.
For instance, the 10% trimmed mean is the average of the 5th to 95th percentile of the data, while the 90% winsorized mean sets the bottom 5% to the 5th percentile, the top 5% to the 95th percentile, and then averages the data. Winsorizing thus does not change the total number of values in the data set, N.
Bootstrapping can be interpreted in a Bayesian framework using a scheme that creates new data sets through reweighting the initial data. Given a set of data points, the weighting assigned to data point in a new data set is =, where is a low-to-high ordered list of uniformly distributed random numbers on [,], preceded by 0 and succeeded by 1.