In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set...

dictionary learning. In unsupervised feature learning, features are learned with unlabelled input data. Examples include dictionary learning, independent...

Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find...

playing Multi-task learning Multitask optimization Transfer of learning in educational psychology Zero-shot learning Feature learning external validity...

Normalization (machine learning) Normalization (statistics) Standard score fMLLR, Feature space Maximum Likelihood Linear Regression...

for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods...

corner or blob Feature (machine learning), in statistics: individual measurable properties of the phenomena being observed Software feature, a distinguishing...

The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year....

has been used as a feature learning (or dictionary learning) step, in either (semi-)supervised learning or unsupervised learning. The basic approach...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

minimization Feature engineering Feature learning Learning to rank Occam learning Online machine learning PAC learning Regression Reinforcement Learning Semi-supervised...

International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

detection. Appearance based object categorization typically contains feature extraction, learning a classifier, and applying the classifier to new examples. There...

(November 2011). "Adaptive deconvolutional networks for mid and high level feature learning". 2011 International Conference on Computer Vision. IEEE. pp. 2018–2025...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was...

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...

meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with...

Processing series. MIT Press. ISBN 978-0-26202617-8. "Unsupervised Feature Learning and Deep Learning Tutorial". ufldl.stanford.edu. Retrieved 2024-03-25. ai-faq...