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Time-series of mel-frequency cepstrum coefficients. 8,800 Text Classification 2010 [124] [125] M. Bedda et al. ISOLET Dataset Spoken letter names. Features extracted from sounds. 7797 Text Classification 1994 [126] [127] R. Cole et al. Japanese Vowels Dataset Nine male speakers uttered two Japanese vowels successively.
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]
Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records.
As an example, consider a dataset of birds for classification. The feature space for the minority class for which we want to oversample could be beak length, wingspan, and weight (all continuous). To then oversample, take a sample from the dataset, and consider its k nearest neighbors (in feature space).
Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries. [6] The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases. [ 6 ]
Classification, Lifelong object recognition, Robotic Vision 2019 [43] Q. She et al. 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.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
time-series-classification (Java) a package for time series classification using DTW in Weka. The DTW suite provides Python and R packages with a comprehensive coverage of the DTW algorithm family members, including a variety of recursion rules (also called step patterns), constraints, and substring matching.