Search results
Results from the WOW.Com Content Network
Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret, [1] shorter training times, [2] to avoid the curse of dimensionality, [3] improve the compatibility of the data with a certain learning model class, [4] to encode inherent symmetries present in the input space. [5] [6] [7] [8]
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.
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 ...
PharmaPack Dataset 1,000 unique classes with 54 images per class. Class labeling, many local descriptors, like SIFT and aKaZE, and local feature agreators, like Fisher Vector (FV). 54,000 Images and .mat files Fine-grain classification 2017 [189] O. Taran and S. Rezaeifar, et al. Stanford Dogs Dataset
In geographic information systems, a feature is an object that can have a geographic location and other properties. [1] Common types of geometries include points , arcs , and polygons . Carriageways and cadastres are examples of feature data.
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]
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).
Other classes of feature engineering algorithms include leveraging a common hidden structure across multiple inter-related datasets to obtain a consensus (common) clustering scheme. An example is Multi-view Classification based on Consensus Matrix Decomposition (MCMD), [ 2 ] which mines a common clustering scheme across multiple datasets.