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一个汇集研究方法的目录——了解每种方法如何运作、何时使用以及它做不到什么。

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以内容为本的研究方法参考文库——每种方法是什么、如何运作、源自何处。

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方法统计学1,836人工智能与机器学习1,661决策科学932研究方法1,354测量1,745因果与证据532研究实践118
1,522 种方法 · 人工智能与机器学习清除
与筛选条件匹配的真实方法。
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network analysis

Dynamic Closeness Centrality

Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network,

2 个来源2010
network analysis

Dynamic Community Detection

Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and com

2 个来源2010
network analysis

Dynamic Degree Centrality

Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profi

2 个来源2012
network analysis

Dynamic Ego Network Analysis

Dynamic ego network analysis examines how the personal network surrounding a focal individual (the ego) changes over time. By collecting the same ego-centered network data at multiple time points, researchers can track tie formation and dissolution, shifts in network composition, and changes in structural properties su

2 个来源1990
network analysis

Dynamic Eigenvector Centrality

Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows.

2 个来源2010
network analysis

Dynamic Exponential Random Graph Model

The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about

2 个来源2010
network analysis

Dynamic Modularity Analysis

Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giv

2 个来源2010
network analysis

Dynamic PageRank

Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history,

2 个来源2007
network analysis

Dynamic Stochastic Block Model

The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural c

2 个来源2011
network analysis

Dynamic Two-Mode Network Analysis

Dynamic two-mode network analysis studies bipartite networks — structures with two distinct node types, such as actors and events or authors and papers — as they evolve over time. By tracking how memberships, affiliations, and co-participations change across temporal snapshots, it reveals the emergence, dissolution, an

2 个来源2000
deep learning

Echo State Network

An Echo State Network (ESN) is a type of recurrent neural network introduced by Herbert Jaeger and Harald Haas in 2004 that exploits a large, randomly connected, fixed recurrent layer — the reservoir — to project input signals into a high-dimensional nonlinear space. Only the linear output weights are trained, typicall

1 个来源2004
machine learning

ECLAT

ECLAT, introduced by Mohammed Zaki in 2000, mines frequent itemsets using a vertical data representation: instead of scanning transactions, it stores for each item the set of transaction IDs (a tidset) that contain it, and computes the support of any itemset by intersecting tidsets. This depth-first, intersection-based

1 个来源2000
electrical engineering

Economic Dispatch

Economic Dispatch (ED) is the process of optimally allocating power output among committed generators to meet demand at minimum fuel cost. Introduced by Kirchmayer in 1958, ED is a fundamental real-time optimization problem solved every few minutes in power system operations. Unlike Unit Commitment (which decides gener

3 个来源1958
particle physics

Effective Field Theory

Effective Field Theory (EFT) is a general framework for studying physics at low energies in terms of the relevant degrees of freedom, without requiring complete knowledge of high-energy physics. By expanding in powers of energy, EFT provides model-independent parameterizations of new physics effects and systematic meth

3 个来源1979
thermodynamics

Effectiveness-NTU Method

The Effectiveness-NTU method is an alternative approach to heat exchanger analysis that measures thermal performance relative to the theoretical maximum possible heat transfer. It is particularly powerful for design problems where outlet temperatures are unknown. The method uses effectiveness (ratio of actual to maximu

2 个来源1984
deep learning

EfficientNet

EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substan

2 个来源2019
network analysis

Ego Network Analysis

Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces

2 个来源1992
network analysis

Eigenvector Centrality

Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a

2 个来源1972
oceanography

Ekman Transport

Ekman transport is the net volume flux of water driven by wind stress balanced with Coriolis force in the surface boundary layer. Derived by Vagn Walfrid Ekman in 1905 from the principle that wind stress is transmitted through the water column in a spiral pattern, Ekman transport is responsible for coastal upwelling an

2 个来源1905
manufacturing

Elastohydrodynamic Lubrication

Elastohydrodynamic lubrication (EHL) is the regime of fluid film lubrication in which elastic deformation of the surfaces plays a crucial role in maintaining a fluid layer between sliding or rolling surfaces. In applications like roller bearings and gears, the contact pressure is extremely high, causing the lubricant v

3 个来源1977
model evaluation

Elbow Method

The Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occur

2 个来源1953
applied physics

Electrochemical Impedance Spectroscopy

Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for characterizing electrochemical systems by applying a small AC voltage over a range of frequencies and measuring the resulting current response. Developed in the late 1960s, EIS reveals the frequency-dependent resistance and capacitance of interfac

3 个来源1969
linguistics

Electropalatography

Electropalatography (EPG) is an instrumental method for measuring tongue-to-palate contact during speech by using a specially designed artificial palate fitted with an array of sensors. Developed by William John Hardcastle in the 1970s, EPG provides detailed real-time visualization of articulation and has applications

3 个来源1974
mining engineering

Electrowinning

Electrowinning is an electrochemical process that extracts and refines metals from dilute leaching solutions by passing electric current through an electrolytic cell. Metal ions migrate to the cathode (negative electrode) and are reduced to pure metal, while impurities remain in solution. This process is essential for

2 个来源1890
mining engineering

Ellingham Diagram

The Ellingham Diagram, introduced by Harold Ellingham in 1944, is a graphical representation of the Gibbs free energy change for oxide formation and reduction as a function of temperature. It is an essential tool for predicting the thermodynamic feasibility of ore reduction and selecting appropriate reducing agents and

2 个来源1944
cryptography

Elliptic Curve Cryptography

Elliptic Curve Cryptography (ECC) is a public-key cryptosystem based on the algebraic structure of elliptic curves over finite fields. Proposed independently by Neal Koblitz and Victor Miller in 1985, ECC offers equivalent security to RSA with much smaller key sizes. Modern cryptography increasingly favors ECC for its

2 个来源1985
biomechanics

EMG Envelope

Electromyography (EMG) envelope analysis extracts the amplitude modulation of muscle electrical activity to quantify muscle activation over time. By filtering and demodulating the raw EMG signal, practitioners obtain a smoothed activation profile that reflects when and how intensely a muscle is contracting during movem

2 个来源1999
text mining

Emotion Detection

Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a smal

2 个来源1992
signal processing

Empirical Mode Decomposition

Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no

1 个来源1998
meteorology

Empirical Orthogonal Teleconnection

Empirical orthogonal function (EOF) analysis is a statistical technique that identifies dominant spatial patterns and temporal variability in atmospheric or oceanic data. When applied to geographically distant locations, EOF analysis reveals teleconnection patterns—coherent patterns of variability that link weather sys

2 个来源1956
time series

Empirical Wavelet Transform

The empirical wavelet transform (EWT) is a data-driven wavelet decomposition method that automatically defines wavelet bases adapted to the frequency content of the signal. Introduced by Jérémie Gilles (2013), it overcomes a key limitation of classical wavelets—which use fixed, predefined bases—by constructing custom w

3 个来源2013
machine learning

Ensemble Active Learning

Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing th

2 个来源1992
machine learning

Ensemble Apriori Algorithm

The Ensemble Apriori Algorithm applies ensemble principles to the classic Apriori frequent-pattern miner by running multiple Apriori instances on different data partitions or parameter settings and merging their rule sets. This approach improves coverage, reduces sensitivity to the minimum-support threshold, and scales

2 个来源1994
machine learning

Ensemble Association Rules

Ensemble Association Rules applies ensemble learning principles to association rule mining: multiple rule sets are discovered from different data subsamples or with varied parameters, then merged and weighted to produce a more stable and complete set of co-occurrence patterns. The approach reduces sensitivity to suppor

2 个来源1990
machine learning

Ensemble Autoencoder Anomaly Detection

Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random ini

2 个来源2017
machine learning

Ensemble Decision Tree

Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification

2 个来源1996
machine learning

Ensemble Federated Learning

Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averagi

2 个来源2017
machine learning

Ensemble Few-shot learning

Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-

2 个来源2019
machine learning

Ensemble Gaussian Mixture Model

Ensemble Gaussian Mixture Model (E-GMM) combines multiple independently fitted Gaussian Mixture Models to improve density estimation, clustering stability, and anomaly detection. By averaging or aggregating the probabilistic outputs of several GMMs — each trained on a different data subset or random initialization — th

2 个来源2000
machine learning

Ensemble Gaussian Process

Ensemble Gaussian Process trains multiple independent GP experts on data subsets or overlapping regions, then combines their posterior predictions — means and variances — into a single probabilistic forecast. This approach retains the calibrated uncertainty estimates of standard GPs while overcoming their O(n³) cubic c

2 个来源2000
machine learning

Ensemble Gradient Boosting

Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on c

2 个来源2001
machine learning

Ensemble HDBSCAN

Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitiv

2 个来源2011
machine learning

Ensemble Isolation Forest

Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method

2 个来源2008
machine learning

Ensemble K-means

Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than an

2 个来源2002
machine learning

Ensemble K-nearest neighbors

Ensemble K-Nearest Neighbors combines multiple KNN models — each trained with a different value of k, distance metric, feature subset, or data bootstrap — and aggregates their predictions by majority vote (classification) or averaging (regression). The approach reduces the high variance inherent in any single KNN model

2 个来源2000
data fusion

Ensemble Kalman Filter

The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than

1 个来源1994
machine learning

Ensemble Metric Learning

Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improv

2 个来源2000
machine learning

Ensemble Naive Bayes

Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes model

2 个来源2000
machine learning

Ensemble One-class SVM

Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts

2 个来源2001
machine learning

Ensemble Online Learning

Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting c

2 个来源2001
machine learning

Ensemble Self-supervised Learning

Ensemble Self-supervised Learning combines multiple self-supervised models, objectives, or augmentation views into a unified framework to produce more robust and generalizable representations from unlabeled data. By aggregating diverse self-supervised signals, the ensemble reduces the risk of representation collapse an

2 个来源2020
machine learning

Ensemble Semi-supervised Learning

Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either

2 个来源1998
machine learning

Ensemble Support Vector Machine

Ensemble Support Vector Machine combines multiple independently trained SVM classifiers or regressors — each fitted on a different data partition, bootstrap sample, or feature subset — and aggregates their outputs via voting, averaging, or stacking. The approach mitigates the high computational cost and sensitivity to

2 个来源2000
machine learning

Ensemble Transfer Learning

Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the

2 个来源2010
text mining

Entity Linking

Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgil

2 个来源2008
bioinformatics

Epigenome-wide association study

An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneou

2 个来源2008
bioinformatics

Epigenome-wide association study in educational research

An epigenome-wide association study (EWAS) applied to educational research scans DNA methylation levels at hundreds of thousands of CpG sites across the genome to identify loci whose methylation is statistically associated with educational attainment, cognitive ability, or related learning outcomes. By linking blood- o

2 个来源2011
astronomy

Epoch of Reionization 21-cm

The 21-centimeter line observation of neutral hydrogen is a powerful technique for studying the Epoch of Reionization, when the first stars and galaxies ionized the intergalactic medium about 13 billion years ago. Proposed by Scott and Rees in 1990, this method probes the universe's transition from the dark ages to the

3 个来源1990
bioinformatics

eQTL Analysis

eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — reve

2 个来源2001
software engineering

Equivalence Partitioning Testing

Equivalence partitioning divides input domains into equivalence classes—sets of inputs expected to behave identically—then selects test cases from each class. Introduced by Myers (1979), this technique reduces test cases while maintaining effectiveness. Boundary value analysis (BVA) complements partitioning by testing

3 个来源1979
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