ScholarGate

探索

文库我的文库桌面预检 Review Studio 助手

工作区

打造您的书架

保存方法、整理合集，并将它们带到您的桌面。

文库 / 浏览

方法库

按方法、领域与证据探索科学。

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。

6,575 方法11 领域7 方法家族40 语言

科学图谱在使用之前，先绘制科学的结构图。领域 · 方法 · 证据路径探索图谱

领域Health & Medicine716 Psychology570 Business & Finance410 Engineering330 Life Sciences263 Education261 Research Practice248 Natural Sciences

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。

开放数据（CC-BY）

探索

文库
搜索方法…
按领域浏览
学科领域
历程
对比
该用哪种方法？

参考

学科
图集
术语表
方法论
哲学

工作区

我的文库
桌面
聊天

公司

关于
价格
联系我们
建议新方法

本词条系根据已发表文献整理，仅供参考。核实任何信息的准确性及其是否适用于您的具体用途，仍由您自行负责。

© 2026 ScholarGate · 研究方法参考文库

隐私
Cookie
条款
删除账户

236

Social Sciences185

Environment & Sustainability160

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。

排序热门程度 A–Z Z–A 最新

electrical engineering

Newton-Raphson Power Flow

The Newton-Raphson method is a powerful iterative technique for solving the nonlinear power flow equations in electrical power systems. Introduced by Tinney and Hart in 1967, it became the industry standard for computing steady-state voltage and power distributions across transmission networks. The method uses Jacobian

3 个来源1967

particle physics

NFW Halo Profile

The Navarro-Frenk-White (NFW) profile is a widely-adopted density profile for dark matter halos emerging from cosmological simulations. It provides a simple parametric description of how dark matter density varies with distance from the halo center, essential for modeling galaxy cluster mass distributions, weak lensing

3 个来源1997

NMF Topic Model

Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, a

2 个来源1999

NMF Topic Modeling

NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpreta

2 个来源1999

machine learning

Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativ

3 个来源1999

Non-stationary Transformer

Non-stationary Transformer is a Transformer-based time-series forecasting architecture introduced by Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long at NeurIPS 2022. It addresses a fundamental tension in applying Transformers to real-world time series: over-stationarization during preprocessing strips out non-stat

1 个来源2022

civil engineering

Nonlinear Time-History Analysis

Nonlinear time-history analysis is a numerical method that solves the equations of motion step-by-step in the time domain, using recorded or synthetic earthquake ground motions as input. Developed by Newmark in 1959, this approach captures the full dynamic response of structures including material nonlinearity, geometr

3 个来源1959

model evaluation

Normalized Mutual Information

Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect ag

1 个来源2005

Normalizing Flows

Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable e

1 个来源2015

nuclear physics

Nuclear Decay Analysis

Nuclear decay analysis is the systematic study of radioactive transformation processes, originating from Rutherford and Soddy's work in the early 1900s. It quantifies the rate and modes of nuclear disintegration using decay constants, half-lives, and branching ratios to predict activity evolution, date samples via radi

2 个来源1900

nuclear physics

Nuclear Fuel Cycle Analysis

Nuclear fuel cycle analysis is a comprehensive assessment of uranium and plutonium flows from extraction through enrichment, power generation, and waste management, originating from Fermi's controlled nuclear reaction. It quantifies resource requirements, energy balances, greenhouse gas emissions, and waste streams to

2 个来源1942

materials science

Nudged Elastic Band Method

The Nudged Elastic Band (NEB) method is a computational technique for finding minimum-energy transition paths between stable atomic configurations and estimating activation barriers. Developed by Jónsson, Mills, and Jacobsen in 1998, NEB connects initial and final states with a chain of images (configurations) held tog

3 个来源1998

Object Detection

Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-h

2 个来源2014

Ocean Color Chlorophyll-a

Ocean color remote sensing is the primary global method for retrieving seawater chlorophyll-a concentrations and phytoplankton productivity from satellite sensors. Based on bio-optical principles established in the 1970s, ocean color algorithms convert satellite spectral reflectance measurements into estimates of chlor

2 个来源1978

Ocean-Atmosphere Coupled Model

An Ocean-Atmosphere Coupled Model (AOGCM) is a comprehensive climate simulation that couples dynamic general circulation models of the atmosphere and ocean with explicit exchange of heat, momentum, and moisture at the interface. Developed by Manabe, Bryan, and colleagues in the 1970s, coupled models are essential for s

2 个来源1975

telecommunications

OFDM

OFDM is a multicarrier modulation technique that divides a wideband channel into many narrowband orthogonal subcarriers. Introduced by Weinstein and Ebert in 1971, it exploits the duality between time and frequency domains to efficiently use spectrum while mitigating intersymbol interference in frequency-selective chan

2 个来源1971

telecommunications

Okumura-Hata Model

The Okumura-Hata model is an empirical propagation model for predicting path loss in mobile radio systems. Developed by Okumura (1968) and mathematically formalized by Hata (1980), it is one of the most widely used models for cellular network planning. The model predicts median path loss as a function of frequency, dis

2 个来源1968

machine learning

One-class SVM

One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the sin

2 个来源1999

machine learning

Online Active learning

Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled da

2 个来源2000

machine learning

Online Association Rules

Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.

2 个来源1996

machine learning

Online Autoencoder Anomaly Detection

Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learn

2 个来源2010

machine learning

Online Boosting

Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real

2 个来源2001

machine learning

Online DBSCAN

Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehou

2 个来源1998

machine learning

Online Decision Tree

An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time

2 个来源2000

machine learning

Online Federated Learning

Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw obs

2 个来源2019

machine learning

Online Few-shot Learning

Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.

2 个来源2019

machine learning

Online FP-growth

Online FP-growth is an incremental extension of the FP-growth algorithm that mines frequent itemsets from continuously arriving transaction streams without rebuilding the full FP-tree from scratch. It updates an existing compact tree structure as new transactions arrive, making it suitable for real-time and high-veloci

2 个来源2004

machine learning

Online Gaussian Mixture Model

Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.

2 个来源2000

machine learning

Online Gaussian Process

Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probab

2 个来源2002

machine learning

Online Gradient Boosting

Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or

2 个来源2011

machine learning

Online HDBSCAN

Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, co

2 个来源2015

machine learning

Online Isolation Forest

Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history.

2 个来源2008

machine learning

Online K-means

Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would

2 个来源1967

machine learning

Online K-nearest neighbors

Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most

2 个来源2010

machine learning

Online Learning

Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is meas

2 个来源1958

machine learning

Online LightGBM

Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-exp

2 个来源2017

machine learning

Online Linear Regression

Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data

2 个来源1960

machine learning

Online Metric Learning

Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale,

2 个来源2004

machine learning

Online Naive Bayes

Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuou

2 个来源2000

machine learning

Online One-class SVM

Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.

2 个来源2006

machine learning

Online Random Forest

Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution chang

2 个来源2009

machine learning

Online Self-supervised Learning

Online Self-supervised Learning (online SSL) trains neural networks on unlabeled data that arrives sequentially or in streams, using automatically generated supervisory signals (pretext tasks) instead of human labels. By updating the model continuously as new data flows in, it enables perpetually evolving representatio

2 个来源2020

machine learning

Online Semi-supervised learning

Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-tim

2 个来源2000

machine learning

Online Support Vector Machine

Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SV

2 个来源2005

machine learning

Online Transfer learning

Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled t

2 个来源2010

machine learning

Online Voting Ensemble

Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from sc

2 个来源2001

Open Information Extraction

Open Information Extraction (Open IE) is a text-mining task that automatically extracts subject-relation-object triples from text without requiring a predefined relation schema. Introduced by Banko and colleagues (2007) for extraction over the open web, it converts free-running text into structured assertions used to b

2 个来源2007

Opinion Mining

Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a s

2 个来源2012

machine learning

OPTICS

OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clust

3 个来源1999

electrical engineering

Optimal Power Flow

Optimal Power Flow (OPF) is a fundamental optimization framework for computing the most economical and secure operating point of an electrical power system. Introduced by Jean Carpentier in 1962, OPF minimizes operational costs (fuel, losses, or other expenses) while satisfying physical and operational constraints. Mod

3 个来源1962

computer vision

ORB Feature Descriptor

ORB (Oriented FAST and Rotated BRIEF) combines the FAST corner detector with the BRIEF binary descriptor to create a fast, rotation-invariant feature detector and descriptor. Introduced by Rublee et al. in 2011, ORB is designed as a free, efficient alternative to patented methods like SIFT and SURF, making it ideal for

2 个来源2011

applied physics

Orbit Determination (Lambert's Problem)

Lambert's problem is a classical astrodynamics boundary-value problem that determines an orbit connecting two points in space given a transfer time. Formulated by Johann Heinrich Lambert in the 18th century, it is fundamental to trajectory design for interplanetary missions and spacecraft maneuvers. The solution provid

3 个来源1761

telecommunications

OSPF

OSPF is a link-state interior gateway protocol (IGP) for routing within an autonomous system. Introduced by John Moy in 1998, OSPF converges faster than distance-vector protocols and supports equal-cost multipath (ECMP). It remains widely deployed in enterprise and ISP networks for intra-domain routing, though IS-IS is

2 个来源1998

machine learning

Out-of-Distribution Detection

Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than

1 个来源2017

network analysis

PageRank

PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Goo

1 个来源1999

Paleomagnetic Analysis

Paleomagnetic analysis is the study of remnant magnetization in rocks and sediments to determine the direction and magnitude of the Earth's ancient magnetic field and to establish the ages and tectonic histories of crustal rocks. Formalized by Fisher (1953) and Butler (1992), paleomagnetism underpins plate tectonics pl

2 个来源1953

Paleomagnetism Analysis

Paleomagnetism analysis is the study of ancient magnetic properties of rocks, measuring fossil magnetization to determine paleomagnetic field history and assign geological ages. Pioneered by Brunhes (1906) and systematized by Tauxe (2010), this method reveals geomagnetic reversals, polar wander paths, and paleomagnetic

3 个来源1906

Pan-Tompkins QRS Detection

The Pan-Tompkins algorithm is a real-time QRS detection method for electrocardiograms (ECGs) that identifies the R-peaks (ventricular depolarization) and QRS complexes from continuous cardiac waveforms. Published by Jiapu Pan and Willis Tompkins in 1985, it remains a standard reference for ECG processing and is widely

2 个来源1985

Paraphrase Detection

Paraphrase detection is a natural-language-processing task that decides whether two sentences expressed in different wordings carry the same meaning. The task and its benchmark resources were established by Dolan and Brockett (2005), and it underpins plagiarism detection, question matching, and data deduplication.

machine learning

Partial Least Squares

Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. T

2 个来源1975

← 1415 / 2616 →