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One catalogue of research methods — learn how each one works, when to use it, and what it can’t do.

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Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

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1,661 methods · AI & MLClear
Real methods matching your filter.
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text mining

Abbreviation Expansion

Abbreviation and acronym resolution is a natural-language-processing pipeline that maps each short form in a text to its full-length definition using contextual cues from the surrounding text. It is especially important in medical, legal, and technical documents, where the same acronym may carry entirely different mean

2 sources2003
model evaluation

Accuracy

Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.

2 sources
international relations

ACLED Event Analysis

ACLED event analysis is the disaggregated study of political violence and protest using the Armed Conflict Location and Event Data project, introduced by Raleigh, Linke, Hegre, and Karlsen (2010). ACLED codes individual events — battles, violence against civilians, riots, protests, explosions and remote violence, and s

1 source2010
architecture

Acoustic Design Analysis

Acoustic Design Analysis is a method for evaluating the acoustical properties of buildings to predict sound levels, reverberation time, and speech intelligibility. Founded by Wallace Clement Sabine in the early 1900s, the field encompasses room acoustic design (controlling reverberation), sound transmission loss (preve

3 sources1922
oceanography

Acoustic Doppler Current Profiler

The Acoustic Doppler Current Profiler (ADCP) is an instrument that uses Doppler-shifted acoustic backscatter to measure water velocity profiles along a vertical profile. Developed by RD Instruments in the 1980s, it has become the standard method for high-resolution current profiling in oceanographic research. ADCPs pro

2 sources1983
linguistics

Acoustic Phonetics

Acoustic Phonetics is the study of the physical properties of speech sounds using instrumentation to measure and analyze sound waves. Pioneered by Peter Ladefoged and Kenneth Stevens, this method uses spectrograms, formant analysis, and waveform measurements to characterize vowels, consonants, and prosodic features wit

3 sources1962
acoustics

Acoustic Ray Tracing

Acoustic ray tracing is a computational technique for predicting sound propagation in rooms by treating acoustic energy as rays that reflect specularly off surfaces. Formalized by Allen and Berkley in 1979 via the image source method, ray tracing is one of the most computationally efficient methods for room acoustic si

3 sources1979
control theory

Active Disturbance Rejection Control

Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requ

2 sources2009
machine learning

Active Learning

Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottlenec

1 source2009
machine learning

Active learning Association rules

Active learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery process interactively. Instead of exhaustively enumerating all rules above a fixed support-confidence threshold, the system selects the most infor

2 sources2010
machine learning

Active Learning Autoencoder Anomaly Detection

Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving stro

2 sources2014
machine learning

Active learning Boosting

Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — a

2 sources1998
machine learning

Active learning Decision tree

Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising c

2 sources1984
machine learning

Active Learning Federated Learning

Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative ex

2 sources2020
machine learning

Active learning Gaussian mixture model

Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing label

2 sources2000
machine learning

Active Learning Gradient Boosting

Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer

2 sources2000
machine learning

Active learning Isolation forest

Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving pre

2 sources2008
machine learning

Active learning K-nearest neighbors

Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far

2 sources1951
machine learning

Active Learning LightGBM

Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled s

2 sources2017
machine learning

Active Learning Linear Regression

Active Learning Linear Regression is an iterative machine-learning approach that couples a linear regression model with an intelligent query strategy to select the most informative unlabeled points for labeling. By focusing labeling effort where uncertainty is highest, it achieves competitive predictive accuracy with f

2 sources1996
machine learning

Active learning One-class SVM

Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves

2 sources2000
machine learning

Active Learning Self-supervised Learning

Active learning combined with self-supervised learning leverages unlabeled data through self-supervised pre-training to build rich representations, then uses an active query strategy to select the most informative examples for human annotation, maximizing model performance under a tight labeling budget. This hybrid app

2 sources2020
machine learning

Active learning Stacking ensemble

Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by

2 sources1992
machine learning

Active learning Support vector machine

Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passiv

2 sources2001
machine learning

Active Learning Voting Ensemble

Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves hig

2 sources1992
human geography

Activity Space Analysis

Activity space analysis measures the geographic area within which an individual moves and carries out their routine daily activities — home, work, shopping, leisure — and the travel that links them. By delineating this lived spatial footprint from observed visit locations, it reveals how far and in what directions peop

1 source1997
machine learning

AdaBoost

AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for c

1 source1997
control theory

Adaptive Control

Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from a

2 sources1983
signal processing

Adaptive LMS Filter

The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filt

2 sources1960
manufacturing

Additive Manufacturing Slicing

Additive manufacturing slicing is the computational process of converting a three-dimensional CAD model into a series of two-dimensional cross-sectional layers that are sequentially built up by 3D printing hardware. Developed during the early maturation of stereolithography and selective laser sintering in the 1990s, t

3 sources1990
model evaluation

Adjusted R-squared

Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predict

3 sources1961
model evaluation

Adjusted Rand Index

The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indic

2 sources1985
genetics

Admixture Analysis

Admixture analysis is a population genetics method that infers population structure and individual ancestry from multilocus genotype data. Originally developed by Pritchard, Stephens, and Donnelly (2000) and refined by Alexander, Novembre, and Lange (2009), admixture analysis reveals how genetic variation is distribute

3 sources2009
applied physics

Adsorption Isotherm (Langmuir-Freundlich)

Adsorption isotherms describe the equilibrium uptake of a substance on a solid surface as a function of gas or solution phase concentration at constant temperature. The Langmuir isotherm (1918) and Freundlich isotherm (1906) are classical empirical models. The Langmuir model assumes monolayer coverage and is mechanisti

3 sources1918
deep learning

Adversarial Training

Adversarial Training is a robust optimization procedure for deep neural networks in which the model is trained not on clean data alone but on worst-case perturbed inputs crafted during training. Formalized by Madry et al. (2018) as a min-max saddle-point problem, the method uses Projected Gradient Descent (PGD) to gene

1 source2018
geophysics

Aerosol Optical Depth

Aerosol Optical Depth (AOD) is a dimensionless measure of aerosol light extinction in the atmosphere, quantifying how much sunlight is scattered and absorbed by particles suspended in air. Formalized by Ångström in 1929 and now routinely measured via satellite (MODIS, Sentinel-5P) and ground networks (AERONET), AOD is

2 sources1929
cryptography

AES (Rijndael)

The Advanced Encryption Standard (AES), also known as Rijndael, is a symmetric block cipher adopted as the official encryption standard by the U.S. government in 2001. It processes data in 128-bit blocks using 128, 192, or 256-bit keys and performs multiple rounds of substitution, permutation, and mixing operations. AE

2 sources2001
machine learning

Affinity Propagation

Affinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messages between every pair of points until a consistent set of clusters emerges. Unlike k-means it does not require the number of clusters to be

1 source2007
simulation

Agent-based cellular automata

Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling

2 sources1986
simulation

Agent-based Discrete-Event Simulation

Agent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-making heterogeneity of individual entities while maintaining the precise, time-stamped flow control of discrete-event simulation, making it

2 sources2000
simulation

Agent-based Markov model

The Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently samples its next state from a probability transition matrix, enabling the model to capture both micro-level heterogeneity across agents and t

2 sources2000
simulation

Agent-based microsimulation

Agent-based microsimulation (ABMS) merges traditional microsimulation's individual-level statistical tracking with agent-based modeling's behavioral rules and interaction mechanisms. It creates virtual populations of heterogeneous agents who evolve over time according to transition probabilities, adaptive behaviors, an

2 sources1957
strategic management

Agent-Based Model of Competitive Strategy

An agent-based model of competitive strategy represents firms as autonomous, heterogeneous, adaptive agents whose decision rules and local interactions generate emergent industry-level dynamics that no single firm designs. Davis, Eisenhardt, and Bingham's 2007 roadmap for developing theory through simulation places thi

1 source2007
simulation

Agent-Based Modeling

Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions

2 sources1970
simulation

Agent-based multi-objective optimization

Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adapti

2 sources1990
simulation

Agent-based scenario analysis

Agent-based scenario analysis embeds agent-based simulation models inside a structured scenario planning framework. Researchers define two to four contrasting future scenarios, configure agent populations and environmental rules to reflect each scenario's assumptions, run the simulation under each condition, and compar

2 sources1990
simulation

Agent-based sensitivity analysis

Agent-based sensitivity analysis (ABSA) applies sensitivity analysis techniques to agent-based models (ABMs) to determine which input parameters most strongly influence emergent outputs. Because ABMs are stochastic and nonlinear, standard analytical derivatives are unavailable; ABSA uses designed simulation experiments

2 sources2000
simulation

Agent-based system dynamics

Agent-based system dynamics (AB-SD) is a hybrid simulation paradigm that couples agent-based modeling (ABM) at the micro level with system dynamics (SD) stock-and-flow structures at the macro level. This allows researchers to capture emergent individual behavior and feedback-driven aggregate dynamics within a single co

2 sources2000
software engineering

Agile Velocity Tracking

Velocity tracking measures the amount of work (typically story points or tasks) a team completes in a sprint, enabling capacity planning, release forecasting, and identification of process improvements. Introduced in Scrum methodology by Schwaber (2002), velocity provides empirical data for realistic sprint planning an

3 sources2002
aerospace

AHRS

An Attitude Heading Reference System (AHRS) is a complete inertial navigation subsystem that estimates and outputs the three-dimensional orientation (attitude) and heading of a vehicle or platform. AHRS combines measurements from accelerometers, gyroscopes, and often magnetometers through sensor fusion algorithms (typi

3 sources1940
model evaluation

Akaike Information Criterion

The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.

3 sources1974
telecommunications

Alamouti Code

The Alamouti code is an elegant space-time coding scheme that provides full transmit diversity using two antennas and a simple linear receiver. Introduced by Siavash Alamouti in 1998, it requires no channel state information at the transmitter, achieves the same bit-error rate as a single-antenna system with receiver d

2 sources1998
deep learning

AlexNet

AlexNet is a deep convolutional neural network (CNN) introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, outstripping the runner-up by more than 10 percentage points and reigniting broad

3 sources2012
international relations

Alliance Network Analysis

Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) —

1 source2012
international relations

Alliance Portfolio Similarity

Alliance portfolio similarity measures how alike two states' overall patterns of alliance commitments are. Each state has a 'portfolio' — the profile of defense pacts, neutrality agreements, ententes, or no tie it holds with every other state — and the similarity of two portfolios is summarized in a single dyadic score

1 source1999
applied physics

Ambisonics

Ambisonics is a full-sphere spatial audio encoding and reproduction technique that captures and reproduces three-dimensional sound fields. Developed by Michael Gerzon in the 1970s, it uses spherical harmonics to represent sound at all directions around a central point. Unlike surround systems that use discrete channels

3 sources1973
genetics

Ancestral State Reconstruction

Ancestral state reconstruction (ASR) is a phylogenetic method that infers the character states (trait values or evolutionary features) of extinct ancestors by analyzing patterns of variation in extant (living) species. Developed by Wayne Maddison and colleagues in the 1990s, ASR uses the phylogenetic tree and observed

3 sources1991
particle physics

Anti-kT Jet Algorithm

The anti-kT jet algorithm, introduced by Cacciari and Salam in 2008, is a sequential recombination jet clustering algorithm widely used in high-energy physics to group final-state particles into jets. Unlike earlier algorithms, anti-kT produces jets with regular cone-like geometries in transverse momentum-rapidity spac

3 sources2008
machine learning

Apriori Algorithm

The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that

2 sources1994
software engineering

Architecture Smell Detection

Architecture smells are recurring patterns in system structure that indicate potential design problems. Introduced by García et al. (2009), these patterns signal violations of architectural principles (modularity, independence, abstraction) at system scale. Detection combines code metrics, dependency analysis, and patt

3 sources2009