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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]
Data from multiple different smart devices for humans performing various activities. None. 43,930,257 Text Classification, clustering 2015 [183] [184] A. Stisen et al. Indoor User Movement Prediction from RSS Data Temporal wireless network data that can be used to track the movement of people in an office. None. 13,197 Text Classification 2016 ...
Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope. [ 5 ] Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes.
This makes predictive coding similar to some other models of hierarchical learning, such as Helmholtz machines and Deep belief networks, which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning is one of the defining features of predictive coding.
In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. [1] [2] It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method allows ...
Example A occurs twice in set 1 because these are chosen with replacement. Bootstrap aggregation (bagging) involves training an ensemble on bootstrapped data sets. A bootstrapped set is created by selecting from original training data set with replacement. Thus, a bootstrap set may contain a given example zero, one, or multiple times.
THz and thermal video data set This multispectral data set includes terahertz, thermal, visual, near infrared, and three-dimensional videos of objects hidden under people's clothes. 3D lookup tables are provided that allow you to project images onto 3D point clouds. More than 20 videos.
Other approaches include solving it as a constrained linear programming problem, [27] making each expert choose the top-k queries it wants (instead of each query choosing the top-k experts for it), [28] using reinforcement learning to train the routing algorithm (since picking an expert is a discrete action, like in RL), [29] etc.