Search results
Results from the WOW.Com Content Network
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition , classification , and regression tasks.
SIFT10M Dataset SIFT features of Caltech-256 dataset. Extensive SIFT feature extraction. 11,164,866 Text Classification, object detection 2016 [36] X. Fu et al. LabelMe: Annotated pictures of scenes. Objects outlined. 187,240 Images, text Classification, object detection 2005 [37] MIT Computer Science and Artificial Intelligence Laboratory
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]
Heart Disease Data Set Attributed of patients with and without heart disease. 75 attributes given for each patient with some missing values. 303 Text Classification 1988 [266] [267] A. Janosi et al. Breast Cancer Wisconsin (Diagnostic) Dataset Dataset of features of breast masses. Diagnoses by physician is given. 10 features for each sample are ...
Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...
The goal of unsupervised feature learning is often to discover low-dimensional features that capture some structure underlying the high-dimensional input data. When the feature learning is performed in an unsupervised way, it enables a form of semisupervised learning where features learned from an unlabeled dataset are then employed to improve ...
Various plots of the multivariate data set Iris flower data set introduced by Ronald Fisher (1936). [1]A data set (or dataset) is a collection of data.In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.
Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).