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Type of supervised learning in machine learning
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually
Multiple_instance_learning
Overview of and topical guide to machine learning
Multi-task learning Multilinear subspace learning Multimodal learning Multiple instance learning Multiple-instance learning Never-Ending Language Learning Offline
Outline_of_machine_learning
Classification problem where multiple labels may be assigned to each instance
In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive
Multi-label_classification
Decentralized machine learning
pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained
Federated_learning
Subset of artificial intelligence
test instance being generated by the model. Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement
Machine_learning
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 technique
learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning
Transfer_learning
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
Method of image retrieval
Search MPEG-7 Multimedia information retrieval Multiple-instance learning Nearest neighbor search Learning to rank Content-based Multimedia Information
Content-based_image_retrieval
Predictive chemical model
machines. An alternative approach uses multiple-instance learning by encoding molecules as sets of data instances, each of which represents a possible molecular
Quantitative structure–activity relationship
Quantitative_structure–activity_relationship
Machine learning paradigm
algorithm towards correct predictions. For instance, if you want a model to identify cats in images, supervised learning would involve feeding it many images
Supervised_learning
Process of acquiring new knowledge
Learning is the process of acquiring new understanding, knowledge, behavior, skills, values, attitudes, and preferences. The ability to learn is possessed
Learning
Process of categorizing documents
Instantaneously trained neural networks Latent semantic indexing Multiple-instance learning Naive Bayes classifier Natural language processing approaches
Document_classification
Categorization of data using statistics
possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features
Statistical_classification
Memorization technique based on repetition
rote learning eschews comprehension, so by itself it is an ineffective tool in mastering any complex subject at an advanced level. For instance, one illustration
Rote_learning
Process by which a gene can code for multiple proteins
manner. Functional genomics and computational approaches based on multiple instance learning have also been developed to integrate RNA-seq data to predict
Alternative_splicing
Use of technology in education to enhance learning and teaching
practice of educational approaches to learning. Educational technology as technological tools and media, for instance massive online courses, that assist
Educational_technology
Mental illness with multiple personality states
Dissociative identity disorder (DID), previously known as multiple personality disorder (MPD), is a dissociative disorder characterized by the presence
Dissociative identity disorder
Dissociative_identity_disorder
Learning methodology
learner has learned is an instance of meaningful learning. Utilization of meaningful learning may trigger further learning, as the relation of a concept
Meaningful_learning
Educational software application
of distance learning. This is the first known instance of the use of materials for independent language study. The concept of e-learning began to develop
Learning_management_system
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
American biomedical engineer (born 1976)
Z., Madabhushi, A., Gao, P., Cong, F., & Lu, C. (2025). When multiple instance learning meets foundation models: Advancing histological whole slide image
Anant_Madabhushi
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)
Chinese-American computer scientist
2006. Yixin Chen, Jinbo Bi and James Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance Selection, IEEE Transactions on Pattern Analysis and
James_Z._Wang
Term in educational psychology
Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin (1956) as "the search
Concept_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
machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Erroneous AI-generated content presented as true
multiple nodes. Text-to-image models, such as Stable Diffusion, Midjourney and others, often produce inaccurate or unexpected results. For instance,
Hallucination (artificial intelligence)
Hallucination_(artificial_intelligence)
Table layout for visualizing performance; also called an error matrix
sounds. In machine learning these matrices show the success of the learning system both in supervised learning and unsupervised learning, where they are
Confusion_matrix
Educational learning method using computer algorithms and AI
courses, training programs, or learning and development programs. Adaptive learning systems have previously been used, for instance, to help students develop
Adaptive_learning
Use of multiple antennas in radio
(/ˈmaɪmoʊ, ˈmiːmoʊ/), or multiple-input multiple-output, is a wireless technology that multiplies the capacity of a radio link using multiple transmit and receive
MIMO
PMID 21605702. Bandyopadhyay S, Ghosh D, Mitra R, Zhao Z (2015). "MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets"
MicroRNA and microRNA target database
MicroRNA_and_microRNA_target_database
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
Research field that lies at the intersection of machine learning and computer security
synthetic data. As machine learning is scaled, it often relies on multiple computing machines. In federated learning, for instance, edge devices collaborate
Adversarial_machine_learning
Neurodevelopmental disorder
of intellectual development and known as learning difficulty (in the United Kingdom and different from learning disability), is a generalized neurodevelopmental
Intellectual_disability
Theories that aim to account for differences in individuals' learning
psychologists have argued that this "is not an instance of learning styles, rather, it is an instance of ability appearing as a style". Likewise, Fleming
Learning_styles
Ongoing, voluntary, and self-motivated pursuit of knowledge
Lifelong learning is the "ongoing, voluntary, and self-motivated" pursuit of learning for either personal or professional reasons. Lifelong learning is important
Lifelong_learning
Study of viral material
of virus-host associations using protein language models and multiple instance learning". PLOS Computational Biology. 20 (11) e1012597. bioRxiv 10.1101/2023
Virome_analysis
Research field in deep learning
deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
associated costs Cost-sensitive machine learning introduces a scalar cost function in order to find one (of multiple) Pareto optimal points in this multi-objective
Cost-sensitive machine learning
Cost-sensitive_machine_learning
and more general to have multiple sources of information with differing reliabilities or areas of expertise. Active learning also assumes that the single
Proactive_learning
Subfield of machine learning
categorized as three main problems in the book Preference Learning: In label ranking, the model has an instance space X = { x i } {\displaystyle X=\{x_{i}\}\,\
Preference_learning
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
Instructional strategy and a type of blended learning
instructional strategy and a type of blended learning. It aims to increase student engagement and learning by having pupils complete readings at home,
Flipped_classroom
Structuring text as input to generative artificial intelligence
model to perform in-context learning can be viewed as an instance of the more general learning-to-learn or meta-learning paradigm Quantifying Language
Prompt_engineering
Study of psychological theories of learning
viewed learning as interacting with incentives in the environment. For instance, Ute Holzkamp-Osterkamp viewed motivation as interconnected with learning. Lev
Psychology_of_learning
Range of neurodevelopmental conditions
disabilities in learning. On the other hand, those individuals who experienced multiple handicapping conditions along with learning disability frequently
Learning_disability
Unintentional learning of complex information
implicit and explicit learning; for instance, research on amnesia often shows intact implicit learning but impaired explicit learning. Another difference
Implicit_learning
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
prejudice. For instance, Walker and Crogan reclassified a Jigsaw class as a control class after the Jigsaw technique failed in this class. Learning by teaching
Jigsaw_(teaching_technique)
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
Relationship between proficiency and experience
a learning curve Proficiency (test score)Experience (hours spent)01234503691215Proficiency (test score)Example of a steep learning curve A learning curve
Learning_curve
Theory of knowledge
2025.{{cite web}}: CS1 maint: multiple names: authors list (link) Derry, Sharon J. (2014). "A Fish Called Peer Learning: Searching for Common Themes"
Constructivism (philosophy of education)
Constructivism_(philosophy_of_education)
Mathematical problem in cryptography
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. It is based on the idea
Learning_with_errors
Paradigm in machine learning
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the
Weak_supervision
Process of replacing missing data with substituted values
multiple imputation use machine learning techniques to improve its performance. MIDAS (Multiple Imputation with Denoising Autoencoders), for instance
Imputation_(statistics)
Type of feedforward neural network
network with two learning layers. Backpropagation was independently developed multiple times in early 1970s. The earliest instance was Seppo Linnainmaa's
Multilayer_perceptron
Channel access method used by various radio communication technologies
Code-division multiple access (CDMA) is a channel access method used by various radio communication technologies. CDMA is an example of multiple access, where
Code-division_multiple_access
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 that
Explainable artificial intelligence
Explainable_artificial_intelligence
Automated recognition of patterns and regularities in data
provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model
Pattern_recognition
2026 information security incident
breach the infrastructure of Hugging Face, a platform hosting machine learning models and datasets. Contributing factors included a lack of log monitoring
OpenAI rogue agent breach of Hugging Face
OpenAI_rogue_agent_breach_of_Hugging_Face
Concept on humans' and animals' use of past learning in present situations
notion of discrimination learning. Generalization is understood to be directly tied to the transfer of knowledge across multiple situations. The knowledge
Generalization_(learning)
Machine-learning process
More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings
Grammar_induction
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
Theory and practice of education
understood as the approach to teaching, is the theory and practice of learning, and how this process influences, and is influenced by, the social, political
Pedagogy
Learning that occurs through observing the behaviour of others
Observational learning is learning that occurs through observing the behavior of others. It is a form of social learning which takes various forms, based
Observational_learning
Use of multiple languages
display and currency; but each instance of the system only supports a single locale. Multilingualised software supports multiple languages for display and
Multilingualism
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 which
Quantum_machine_learning
Method for discovering interesting relations between variables in databases
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended
Association_rule_learning
Network security model
is the network security model necessary to accommodate the emergence of multiple perimeters and moving parts on the network, and increasingly advanced threats
Real-time_adaptive_security
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)
Distance education using mobile device technology
M-learning, or mobile learning, is defined as "learning across multiple contexts, through social and content interactions, using personal electronic devices"
M-learning
United States special education classification
For instance, if a student has a developmental disability, emotional disabilities, and a visual impairment, they may be classified as having multiple disabilities
Multiple_disabilities
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)
Transmission Control Protocol technology
distributed edge learning applications. At the session layer, the Mobile Access Router project experimented in 2003 with the aggregation of multiple wireless
Multipath_TCP
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
Pedagogical approach
language learning has its origins in communicative language teaching, and is a subcategory of it. Educators adopted task-based language learning for a variety
Task-based_language_teaching
Method in education
assessment for learning, including diagnostic testing, is a range of formal and informal assessment procedures conducted by teachers during the learning process
Formative_assessment
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
Type of machine learning model
A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing
Large_language_model
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
Flaw in mathematical modelling
possible to reconstruct details of individual training instances from an overfitted machine learning model's training set. This may be undesirable if, for
Overfitting
Loss-of-control incident at OpenAI
implement better security measures and take a two-week pause on reinforcement learning training of new AI models. From May to July 2026, AI agents at OpenAI repurposed
2026 cyberattacks by rogue OpenAI agents
2026_cyberattacks_by_rogue_OpenAI_agents
Generalization of definite integrals to functions of multiple variables
multivariable calculus), a multiple integral is a definite integral of a function of several real variables, for instance, f(x, y) or f(x, y, z). Integrals
Multiple_integral
Problem in machine learning and statistical classification
machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one
Multiclass_classification
Multiple hypotheses explain the possible connections between sleep and learning in humans. Research indicates that sleep does more than allow the brain
Sleep_and_learning
Type of feedforward neural network
learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different
Convolutional_neural_network
Pedagogical approach
Computer-supported collaborative learning (CSCL) is a pedagogical approach wherein learning takes place via social interaction using a computer or through
Computer-supported collaborative learning
Computer-supported_collaborative_learning
Fundamental unit of cognition
mechanisms include associative learning, in which similarities are gradually noticed as learners encounter instances, and hypothesis testing, which involves
Concept
Example of the learning curve effect on performance
distributions and shapes of learning curves: A test of the instance theory of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition
Power_law_of_practice
Type of machine learning method
to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization
Lazy_learning
Diagnostic plot of binary classifier ability
increasingly used in machine learning and data mining research. A classification model (classifier or diagnosis) is a mapping of instances between certain classes/groups
Receiver operating characteristic
Receiver_operating_characteristic
Process in which a first language is being acquired
learning mechanism could explain a wide range of language structure acquisition phenomena. Statistical learning theory suggests that, when learning language
Language_acquisition
Smooth approximation of one-hot arg max
possible outcomes. It is a generalization of the logistic function to multiple dimensions and is used in multinomial logistic regression. The softmax
Softmax_function
Machine learning technique
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as
Gradient_boosting
Extracting features from raw data for machine learning
feature matrices for machine learning. MCMD: An open-source feature engineering algorithm for joint clustering of multiple datasets. OneBM or One-Button
Feature_engineering
to such ideas even after they have suffered decisive refutations. For instance, in the 1950s, Festinger, Riecken, and Schachter joined a cult whose members
Conceptual_change
Request for information
grammatical forms, typically used to express them. Rhetorical questions, for instance, are interrogative in form but may not be considered bona fide questions
Question
Adaptive boosting based classification algorithm
be used in conjunction with many types of learning algorithm to improve performance. The output of multiple weak learners is combined into a weighted
AdaBoost
Class of artificial neural network
expensive online variant is called "Real-Time Recurrent Learning" or RTRL, which is an instance of automatic differentiation in the forward accumulation
Recurrent_neural_network
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