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Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Extracting features from raw data for machine learning
In supervised machine learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set
Feature_engineering
Representation learning technique
In machine learning, embedding is a representation learning technique that maps complex, high-dimensional data into a lower-dimensional vector space of
Embedding_(machine_learning)
Overview of and topical guide to machine learning
outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer
Outline_of_machine_learning
Process of automating the application of machine learning
amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection
Automated_machine_learning
Set of learning techniques in machine learning
In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations
Representation_learning
Academic conference in machine learning
The International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Ensemble learning method
In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single
Boosting_(machine_learning)
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
Machine learning strategy
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)
Active learning (machine learning)
Active_learning_(machine_learning)
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks
Quantum_machine_learning
Machine learning technique
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
Attention_(machine_learning)
Phase transition in machine learning
In machine learning, grokking, or delayed generalization, is a phenomenon observed in some settings where a model abruptly transitions from overfitting
Grokking_(machine_learning)
Topics referred to by the same term
corner or blob Feature (machine learning), in statistics: individual measurable properties of the phenomena being observed Software feature, a distinguishing
Feature
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
Plot of machine learning model performance over time or experience
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and
Learning curve (machine learning)
Learning_curve_(machine_learning)
A feature store is a centralized repository used in machine learning to store, manage, and serve features for model training and inference. It provides
Feature_store
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Concept in machine learning
In machine learning, the term tensor informally refers to two different concepts: (i) a way of organizing data and (ii) a multilinear (tensor) transformation
Tensor_(machine_learning)
Statistics and machine learning technique
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from
Ensemble_learning
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
Computational model used in machine learning
In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological
Neural network (machine learning)
Neural_network_(machine_learning)
Technique combining machine learning and computer vision
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
Geometric_feature_learning
Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data
Transformer_(deep_learning)
AI that learns decision rules from data
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves
Rule-based_machine_learning
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
Method used to normalize the range of independent variables
|^{p})^{1/p}}}\right)} Normalization (machine learning) Normalization (statistics) Standard score fMLLR, Feature space Maximum Likelihood Linear Regression
Feature_scaling
Categorization of data using statistics
variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features (grouped into a feature vector)
Statistical_classification
Method of machine learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update
Online_machine_learning
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Automated recognition of patterns and regularities in data
Variable and Feature Selection. The Journal of Machine Learning Research, Vol. 3, 1157-1182. Link Archived 2016-03-04 at the Wayback Machine Iman Foroutan;
Pattern_recognition
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based
Supervised_learning
Microsoft open source gradient boosting framework for machine learning
for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft
LightGBM
Software for understanding biological data
in unanticipated ways. Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature detection and selection
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Type of artificial neural network
learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with
Extreme_learning_machine
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Model-free reinforcement learning algorithm
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
Q-learning
AI whose outputs can be understood by humans
overlapping with interpretable AI, interpretable machine learning and explainable machine learning (XML), is a field of research that explores methods
Explainable artificial intelligence
Explainable_artificial_intelligence
Machine learning method for concept approximation
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It
Semantic analysis (machine learning)
Semantic_analysis_(machine_learning)
Machine learning software
Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon
Tanagra_(machine_learning)
Set of techniques in machine learning
Feature Importance (or Variable Importance or Feature Attribution) refers to a set of techniques and mathematical frameworks used in machine learning
Feature_Importance
Machine learning paradigm
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
Self-supervised_learning
Research institute in Adelaide, South Australia
for Machine Learning (AIML) is a research institute focused on artificial intelligence (AI), computer vision, deep learning and machine learning. It is
Australian Institute for Machine Learning
Australian_Institute_for_Machine_Learning
Machine learning technique
Random features (RF) are a technique used in machine learning to approximate kernel methods, introduced by Ali Rahimi and Ben Recht in their 2007 paper
Random_feature
Machine-learning and computational-neuroscience conference
Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Machine learning method
Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching
Logic_learning_machine
Problem setup in machine learning
Zero-shot learning (ZSL) is a problem setup in machine learning where, at test time, a learner observes samples from classes which were not observed during
Zero-shot_learning
Framework for machine learning
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory
Statistical_learning_theory
Intelligence in machines
develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximise
Artificial_intelligence
Machine learning methods using multiple input modalities
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
Multimodal_learning
Software user interface
context of machine learning.It is also used in conversational AI to manage complex interactions that require human empathy. In machine learning, HITL is
Human-in-the-loop
Machine learning paradigm using minimal training data
Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small
Few-shot_learning
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
Class of artificial neural network
dimensionality reduction, classification, collaborative filtering, feature learning, topic modelling, immunology, and even many‑body quantum mechanics
Restricted_Boltzmann_machine
Property of a model
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions
Bias–variance_tradeoff
Deep learning generative model to encode data representation
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013
Variational_autoencoder
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
Academic journal
The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the
Journal of Machine Learning Research
Journal_of_Machine_Learning_Research
Neural network that learns efficient data encoding in an unsupervised manner
applied to many problems, including facial recognition, feature detection, anomaly detection, and learning the meaning of words. In terms of data synthesis,
Autoencoder
Type of artificial intelligence-based expert system software
content. Advanced bots feature machine learning for dynamic learning of patterns of the opponent as well as dynamic learning of previously unknown maps
Video_game_bot
machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Class of algorithms for pattern analysis
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These
Kernel_method
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Type of database that uses vectors to represent other data
can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word
Vector_database
Approach to machine learning lifecycle management
deploy and maintain machine learning models in production reliably and efficiently. It bridges the gap between machine learning development and production
MLOps
Theory of machine learning
Theoretical results in machine learning often focus on a type of inductive learning known as supervised learning. In supervised learning, an algorithm is provided
Computational_learning_theory
Research field in deep learning
traditional machine-learning techniques, such as support vector machines or random forests. Such descriptors ranged from new techniques for feature engineering
Topological_deep_learning
Open-source software library developed by Yandex
available on GitHub. InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other
CatBoost
Paradigm in machine learning that uses no classification labels
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled
Unsupervised_learning
Optimization algorithm
become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective
Stochastic_gradient_descent
Method in machine learning
called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and
Bootstrap_aggregating
Tuning parameter (hyperparameter) in optimization
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration
Learning_rate
Method of machine learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge
Incremental_learning
Overview of and topical guide to deep learning
intelligence History of machine learning Timeline of machine learning Artificial neural network Representation learning Feature learning Gradient descent Backpropagation
Outline_of_deep_learning
Type of feedforward neural network
support for machine learning algorithms, written in C and Lua. Attention (machine learning) Circuit (neural network) Convolution Deep learning Natural-language
Convolutional_neural_network
Integrated circuit technology
digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing
Neuromorphic_computing
Method in natural language processing
meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to
Word_embedding
of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is
Machine learning in earth sciences
Machine_learning_in_earth_sciences
Vectorizing features using a hash function
In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing
Feature_hashing
Tree-based ensemble machine learning methods
Boosting – Ensemble learning method Decision tree learning – Machine learning algorithm Ensemble learning – Statistics and machine learning technique Gradient
Random_forest
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
Subfield of machine learning
Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of
Meta-learning (computer science)
Meta-learning_(computer_science)
Process in machine learning and statistics
In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction
Feature_selection
Machine learning framework
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was
Generative adversarial network
Generative_adversarial_network
Suite of machine learning software written in Java
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public
Weka_(software)
Machine learning software library
TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training
TensorFlow
Type of artificial neural network
model". The Journal of Machine Learning Research. 3: 1137–1155. Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple
Feedforward_neural_network
Method of measuring prediction error
prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling
Out-of-bag_error
Tasks in machine learning
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Interatomic potentials constructed by machine learning programs
Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed using machine learning
Machine-learned interatomic potential
Machine-learned_interatomic_potential
2020 non-fiction book by Brian Christian
The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian. It is based on numerous interviews
The_Alignment_Problem
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FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
Female
Native American
Native American Hopi name KACHINA means "sacred dancer; spirit."
Female
English
Feminine form of English Max, MAXINE means either "the greatest rival" or "the stream of Mack."Â
Female
Hawaiian
Hawaiian name MAHINA means "moon; moonlight."
Girl/Female
Australian, Japanese
Child of Machi
Female
Scottish
Feminine form of Scottish Lachlan, LACHINA means "lake-land."
Surname or Lastname
English
English : variant spelling of Machen.Spanish (MachÃn) : probably a nickname from machÃn ‘boor’, ‘lout’, often applied to a blacksmith’s apprentice.French : nickname from Old French machin ‘scheming’.
Female
French
French feminine form of Latin Martinus, MARTINE means "of/like Mars."Â
Male
English
Pet form of English Sacheverell, SACHIE means "roe-buck leap."
Male
English
Variant spelling of English unisex Macey, MACIE means "gift of God."
Male
Scottish
Pet form of Scottish Gaelic Lachlann, LACHIE means "lake-land."
Female
French
Feminine form of French Marin, MARINE means "of the sea."
Female
German
German form of Scottish Malvina, MALWINE means "smooth-brow."
Female
Yiddish
(×™Ö·×—Ö°× Ö¶×¢) Yiddish form of Hebrew Yochana, YACHNE means "God is gracious."Â
Girl/Female
Hindu, Indian, Kannada, Marathi, Tamil, Telugu
Feature; Future
Girl/Female
Bengali, Indian
Machine
Male
Hebrew
Variant spelling of Hebrew Yakiyn, YACHIN means "he establishes" or "whom God strengthens."Â
Female
English
Variant spelling of English Maureen, MAURINE means "obstinacy, rebelliousness" or "their rebellion."
Male
Hindi/Indian
(सचिन) Hindi myth name borne by Indra, SACHIN means "pure."
Male
French
French form of Latin Macarius, MACAIRE means "blessed."
Boy/Male
American, Australian
Weighing Machine
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
FEATURE MACHINE-LEARNING
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