ScholarGate

見つける

ライブラリマイライブラリデスク事前審査 Review Studio アシスタント

ワークスペース

本棚を作りましょう

手法を保存し、コレクションを整理して、デスクへ持ち運べます。

アカウント作成

ライブラリ / 参照

ライブラリ

手法・分野・エビデンスから科学を探索する。

研究手法をまとめたひとつのカタログ — 各手法の仕組み、使いどころ、そしてできないことを学べます。

6,516 手法11 分野7 手法ファミリー40 言語

科学アトラス使う前に、科学の構造を地図にする。分野 · 手法 · エビデンスの道筋マップを見る

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

研究手法のためのコンテンツ重視のレファレンスライブラリ — 各手法とは何か、どのように機能し、どの文献に由来するのか。

オープンデータ(CC-BY)

見つける

ライブラリ
手法を検索…
分野から探す
分野
歩み
比較
どの手法を使う?

リファレンス

分野
アトラス
用語集
方法論
哲学

ワークスペース

マイライブラリ
デスク
チャット

会社

サイトについて
料金
お問い合わせ
手法を提案する

各項目は参照を目的として公開資料からまとめられたものです。情報の正確性および利用目的への適合性の確認は、利用者ご自身の責任において行ってください。

© 2026 ScholarGate · 研究手法のレファレンスライブラリ

プライバシー
クッキー
利用規約
アカウントを削除

236

Social Sciences185

Environment & Sustainability160

手法統計学1,836 人工知能・機械学習1,661 意思決定科学932 研究方法1,354 測定1,745 因果とエビデンス532 研究実践118

1,522 件の手法 · 人工知能・機械学習クリア

フィルターに一致する実在の手法。

並べ替え人気順 A–Z Z–A 新着順

civil engineering

Equivalent Static Analysis

Equivalent static analysis is the simplest seismic design method, representing earthquake effects as a single static lateral force applied at the center of mass or distributed over the building height. Standardized by SEAOC in 1959 and incorporated into modern building codes, it is the most commonly used method for des

3件の出典1959

Eulerian-Lagrangian Model

The Eulerian-Lagrangian Model (ELM) is a framework for simulating multiphase flows by treating the continuous phase (liquid or gas) using Eulerian descriptions (fixed grid) and discrete dispersed phases (particles, droplets, bubbles) using Lagrangian tracking. Developed by Crowe and collaborators in 1977, this approach

3件の出典1977

Event Detection

Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and

Exergoeconomic Analysis

Exergoeconomic analysis combines thermodynamics and economics by assigning monetary costs to exergy streams. It reveals how thermodynamic irreversibilities translate into economic losses within industrial systems. This approach enables engineers to identify the most economically significant inefficiencies and make info

2件の出典1993

Exergoenvironmental Analysis

Exergoenvironmental analysis extends exergy-based methods to quantify and allocate environmental impacts of thermal systems. It assigns environmental costs to exergy streams based on upstream lifecycle impacts, revealing which components contribute most significantly to environmental burdens. This enables engineers to

2件の出典2009

Exoplanet Transmission Spectroscopy

Transmission spectroscopy is a technique for studying the atmospheres of exoplanets by analyzing the light passing through the planetary atmosphere during transit. Pioneered by David Charbonneau in 2002 with the detection of sodium in HD 209458b's atmosphere, this method has become the primary tool for characterizing e

3件の出典2002

machine learning

Explainable Association Rules

Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs a

2件の出典1993

machine learning

Explainable Autoencoder Anomaly Detection

Explainable Autoencoder Anomaly Detection augments a standard autoencoder-based anomaly detector with an interpretability layer — such as SHAP values or feature-wise reconstruction error decomposition — that identifies which input features drove the anomaly flag for each observation, turning an opaque reconstruction-er

2件の出典2017

machine learning

Explainable Decision Tree

An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders

2件の出典1984

Explainable Diffusion Model

An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as

2件の出典2020

machine learning

Explainable FP-Growth

Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific

2件の出典2000

Explainable GAN

Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled l

2件の出典2019

machine learning

Explainable Gaussian Mixture Model

An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, a

2件の出典1995

machine learning

Explainable Gaussian Process

An Explainable Gaussian Process (XAI-GP) combines the probabilistic, uncertainty-aware predictions of a Gaussian Process model with systematic interpretability tools — such as SHAP values, kernel decomposition, or sensitivity analysis — so that every prediction comes with both a calibrated confidence interval and an au

2件の出典2006

Explainable Graph Neural Network

Explainable Graph Neural Networks (XAI-GNN) combine standard GNN architectures with post-hoc or intrinsic explanation techniques that reveal which nodes, edges, and node features drove a model's prediction. Pioneered by GNNExplainer (Ying et al., 2019), the field addresses the black-box critique of GNNs and is essentia

2件の出典2019

Explainable GRU

Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's abilit

2件の出典2014

machine learning

Explainable HDBSCAN

Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, a

2件の出典2017

Explainable Image Classification

Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is t

2件の出典2016

Explainable Instance Segmentation

Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by

2件の出典2017

machine learning

Explainable Isolation Forest

Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with

2件の出典2008

machine learning

Explainable K-Means

Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequenc

2件の出典2020

machine learning

Explainable K-Nearest Neighbors

Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and audita

2件の出典1967

Explainable LDA Topic Model

Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science

2件の出典2003

machine learning

Explainable LightGBM

Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability ar

2件の出典2017

Explainable LSTM

Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparen

2件の出典2017

machine learning

Explainable Naive Bayes

Explainable Naive Bayes extends the classic probabilistic Naive Bayes classifier with transparent, human-readable explanations of its predictions. By surfacing class priors, per-feature likelihoods, and log-odds contributions, it offers the interpretability demanded in high-stakes domains such as medicine, law, and edu

2件の出典1950

Explainable Named Entity Recognition

Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity la

2件の出典2018

Explainable NMF Topic Model

An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human reade

2件の出典2001

Explainable Object Detection

Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its deci

2件の出典2016

machine learning

Explainable One-Class SVM

Explainable One-Class SVM pairs the classic One-Class Support Vector Machine anomaly detector — which learns a tight boundary around normal data without requiring labeled anomalies — with post-hoc explainability methods such as SHAP or LIME to reveal which features drive each novelty or anomaly score, converting an opa

2件の出典1999

Explainable Question Answering

Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustwo

2件の出典2016

machine learning

Explainable Random Forest

Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable fe

2件の出典2001

Explainable Recurrent Neural Network

An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrific

2件の出典2017

Explainable Reinforcement Learning

Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling

2件の出典2018

Explainable Semantic Segmentation

Explainable Semantic Segmentation (XSS) couples pixel-wise scene parsing — assigning a class label to every pixel in an image — with post-hoc or intrinsic explanation methods such as Grad-CAM, attention maps, or SHAP, so that the network's class decisions can be audited, visualized, and justified to domain experts in m

2件の出典2019

Explainable Sentence Embeddings

Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a giv

2件の出典2016

Explainable Sentiment Analysis

Explainable sentiment analysis pairs a sentiment classification model — typically a fine-tuned transformer such as BERT or RoBERTa — with a post-hoc or intrinsic explanation method (SHAP, LIME, attention visualization, or integrated gradients) that reveals which words, phrases, or features drove each prediction. The go

2件の出典2016

machine learning

Explainable Stacking Ensemble

Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the acc

2件の出典1992

machine learning

Explainable Support Vector Machine

Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes d

2件の出典2016

Explainable Text Summarization

Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in mo

2件の出典2019

Explainable Topic Modeling

Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond

2件の出典2003

Explainable Transformer

An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy wi

2件の出典2017

Explainable Variational Autoencoder

An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to

2件の出典2013

machine learning

Explainable Voting Ensemble

An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combi

2件の出典2016

machine learning

Explainable XGBoost

Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature

2件の出典2016

network analysis

Exponential Random Graph Model

The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Fran

2件の出典1986

Extended Kalman Filter

The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aeros

3件の出典1961

machine learning

Extra Trees

Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresh

2件の出典2006

model evaluation

F-beta Score

The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1.

2件の出典1979

F-statistics (FST)

F-statistics are a family of measures developed by Sewall Wright to quantify population genetic structure and the degree of genetic differentiation between populations. FST, the most widely used F-statistic, measures the proportion of total genetic variation attributable to differences between populations versus within

3件の出典1951

model evaluation

F1-Score

The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.

2件の出典1979

numerical methods

Fagan Inspection

Fagan Inspection is a formal, structured code review process developed by Michael Fagan at IBM in 1976 that systematically identifies defects before testing. Using defined roles and checklists, Fagan inspections are far more effective at catching bugs than ad-hoc reviews; studies show 70–90% defect detection rate.

3件の出典1976

machine learning

Fairness-Aware ML

Fairness-Aware Machine Learning is a family of techniques that train, constrain, or post-process predictive models so that their error rates or outcomes are equitable across protected demographic groups such as race, gender, or age. The foundational framework of equalized odds and equality of opportunity was formalized

1件の出典2016

Fake News Detection

Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles i

electrical engineering

Fast Decoupled Power Flow

The Fast Decoupled Load Flow (FDLF) method, introduced by Stott and Alsac in 1972, exploits the weak coupling between active and reactive power in power systems to accelerate convergence beyond standard Newton-Raphson. By decoupling the equations and using constant, approximate Jacobians, it reduces computation per ite

3件の出典1972

numerical methods

Fast Multipole Method

The Fast Multipole Method (FMM) is a hierarchical algorithm that reduces the computational complexity of particle interactions from O(n²) to O(n log n) or O(n), developed by Greengard and Rokhlin in 1987. By grouping distant particles and approximating their cumulative effects via multipole expansions, FMM enables effi

3件の出典1987

Faster R-CNN

Faster R-CNN is a two-stage deep convolutional object detection framework introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun (Microsoft Research) at NeurIPS 2015. It replaces the slow selective-search region proposal step used in its predecessors R-CNN and Fast R-CNN with a learned Region Proposal Netw

3件の出典2015

FastText

FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to

3件の出典2016

electrical engineering

Fault Analysis in Power Systems

Fault analysis determines the magnitude and distribution of currents and voltages during abnormal conditions in power systems, such as short circuits. Using Fortescue's symmetrical components method (1918), engineers calculate fault currents to design protection relays and equipment ratings. It is essential for ensurin

3件の出典1918

FEA Bone Remodeling

Finite element analysis (FEA) for bone remodeling predicts how bone tissue density and architecture adapt to changes in mechanical loading over time. Pioneered by Rik Huiskes and Donald Carter in the 1980s, this computational approach integrates stress analysis with biophysical remodeling rules to simulate the long-ter

2件の出典1987

← 67 / 268 →