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...

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

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

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

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...

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

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

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

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

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

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

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...

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

(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, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

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

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

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)...

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...

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...

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

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

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

programs, materials or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems...

learning process. The leakage causes can be sub-classified into two possible sources of leakage for a model: features and training examples. Feature or...

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude...

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

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

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...