ScholarGate
Khám phá
Thư việnThư viện của tôiBànKiểm tra trướcReview StudioTrợ lý
Không gian làm việc
So sánh
Xây dựng kệ sách của bạn

Lưu phương pháp, sắp xếp bộ sưu tập và mang chúng đến bàn của bạn.

Tạo tài khoản
Thư viện
 / Duyệt
Đăng nhập
Thư viện

Khám phá khoa học theo phương pháp, lĩnh vực và bằng chứng.

Một danh mục duy nhất về các phương pháp nghiên cứu — tìm hiểu cách mỗi phương pháp hoạt động, khi nào nên dùng và điều nó không làm được.

6,435 phương pháp11 lĩnh vực7 họ phương pháp40 ngôn ngữ
Atlas khoa họcLập bản đồ cấu trúc của khoa học trước khi sử dụng.Lĩnh vực · phương pháp · lộ trình bằng chứngKhám phá bản đồ
Lĩnh vựcHealth & Medicine716Psychology570Business & Finance410Engineering330Life Sciences263Education261Research Practice
ScholarGate

Thư viện tham khảo về phương pháp nghiên cứu, đặt nội dung lên hàng đầu — mỗi phương pháp là gì, hoạt động ra sao và bắt nguồn từ đâu.

Dữ liệu mở (CC-BY)

Khám phá

  • Thư viện
  • Tìm phương pháp…
  • Duyệt theo lĩnh vực
  • Lĩnh vực
  • Hành trình
  • So sánh
  • Nên dùng phương pháp nào?

Tham khảo

  • Lĩnh vực
  • Bản đồ
  • Bảng thuật ngữ
  • Phương pháp luận
  • Triết học

Không gian làm việc

  • Thư viện của tôi
  • Bàn
  • Trò chuyện

Công ty

  • Giới thiệu
  • Bảng giá
  • Liên hệ
  • Đề xuất phương pháp

Các mục từ được biên soạn từ những nguồn đã công bố nhằm mục đích tham khảo. Việc kiểm chứng tính chính xác và mức độ phù hợp của bất kỳ thông tin nào cho mục đích sử dụng của bạn vẫn thuộc trách nhiệm của bạn.

© 2026 ScholarGate · Thư viện tham khảo về phương pháp nghiên cứu
  • Quyền riêng tư
  • Cookie
  • Điều khoản
  • Xóa tài khoản
248
Natural Sciences236
Social Sciences185
Environment & Sustainability160
Law30
Phương phápThống kê1,836Trí tuệ nhân tạo & học máy1,661Khoa học quyết định932Phương pháp nghiên cứu1,354Đo lường1,745Nhân quả & bằng chứng532Thực hành nghiên cứu118
1,522 phương pháp · Trí tuệ nhân tạo & học máyXóa
Các phương pháp thực khớp với bộ lọc của bạn.
Sắp xếpĐộ phổ biếnA–ZZ–AMới nhất
machine learning

Semi-supervised Apriori Algorithm

The Semi-supervised Apriori algorithm extends the classic Apriori frequent-itemset miner by injecting background knowledge or labeled constraints — such as must-link pairs, forbidden items, or user-specified minimum support thresholds per group — to bias discovery toward practically meaningful association rules and red

2 nguồn1999
machine learning

Semi-supervised Association Rules

Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically mea

2 nguồn2003
machine learning

Semi-supervised Autoencoder Anomaly Detection

Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully s

2 nguồn2018
deep learning

Semi-supervised BERT-based Classification

Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotatio

2 nguồn2019
machine learning

Semi-supervised Boosting

Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone wh

2 nguồn1999
machine learning

Semi-supervised CatBoost

Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow.

2 nguồn2018
deep learning

Semi-supervised Convolutional Neural Network

A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by sc

2 nguồn2013
machine learning

Semi-supervised DBSCAN

Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retain

2 nguồn2000
machine learning

Semi-supervised Decision Tree

A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve bett

2 nguồn2000
deep learning

Semi-supervised Diffusion Model

A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-condit

2 nguồn2020
deep learning

Semi-supervised Doc2Vec

Semi-supervised Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) by training dense document embeddings on both labeled and unlabeled corpora simultaneously, using available class labels as an auxiliary signal to steer the representation toward task-relevant structure while still exploiting the fu

2 nguồn2014
machine learning

Semi-supervised Federated learning

Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised tech

2 nguồn2020
machine learning

Semi-supervised Few-shot Learning

Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it ac

2 nguồn2018
machine learning

Semi-supervised FP-growth

Semi-supervised FP-growth extends the classical Frequent Pattern growth algorithm by incorporating partial labels, user-defined constraints, or class-level information to guide frequent itemset discovery. Instead of mining all patterns indiscriminately, it focuses on patterns that are both statistically frequent and se

2 nguồn2000
deep learning

Semi-supervised GAN

Semi-supervised GAN (SGAN) extends the standard GAN discriminator to simultaneously classify labeled examples into K real classes and detect generated fakes as a (K+1)-th class, letting the generator's synthetic data act as implicit regularization and allowing strong classifiers to be trained with very few labeled exam

2 nguồn2016
machine learning

Semi-supervised Gaussian Mixture Model

The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective l

2 nguồn2000
machine learning

Semi-supervised Gaussian Process

Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purel

2 nguồn2004
machine learning

Semi-supervised Gradient Boosting

Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into trainin

2 nguồn2006
deep learning

Semi-supervised Graph Neural Network

A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classifi

2 nguồn2017
deep learning

Semi-supervised GRU

Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled e

2 nguồn2014
machine learning

Semi-supervised HDBSCAN

Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster

2 nguồn2017
deep learning

Semi-supervised Image Classification

Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically re

2 nguồn2013
deep learning

Semi-supervised Instance Segmentation

Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves comp

2 nguồn2018
machine learning

Semi-supervised Isolation Forest

Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging

2 nguồn2013
machine learning

Semi-supervised K-means

Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful cl

2 nguồn2001
machine learning

Semi-supervised K-nearest neighbors

Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive ma

2 nguồn2002
deep learning

Semi-supervised LDA Topic Model

Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully super

2 nguồn2009
machine learning

Semi-supervised Learning

Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manu

2 nguồn1970
machine learning

Semi-supervised LightGBM

Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-

2 nguồn2017
machine learning

Semi-supervised Linear Regression

Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy tha

2 nguồn2005
deep learning

Semi-supervised LSTM

Semi-supervised LSTM combines the sequential memory of Long Short-Term Memory networks with semi-supervised learning strategies — using a small labeled dataset alongside a large pool of unlabeled sequences. The model is pretrained or regularized on unlabeled data, then fine-tuned on labeled examples, delivering strong

2 nguồn2015
machine learning

Semi-supervised Metric Learning

Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervi

2 nguồn2007
deep learning

Semi-supervised Multilayer Perceptron

A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it ext

2 nguồn2006
machine learning

Semi-supervised Naive Bayes

Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially b

2 nguồn2000
deep learning

Semi-supervised NMF Topic Model

Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical p

2 nguồn2001
deep learning

Semi-supervised Object Detection

Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden whil

2 nguồn2020
machine learning

Semi-supervised One-class SVM

Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly

2 nguồn2001
machine learning

Semi-supervised Online Learning

Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is e

2 nguồn2000
deep learning

Semi-supervised Question Answering

Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong p

2 nguồn2006
machine learning

Semi-supervised Random Forest

Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progr

2 nguồn2009
deep learning

Semi-supervised Reinforcement Learning

Semi-supervised reinforcement learning (SSRL) combines standard reinforcement learning — where an agent learns from sparse reward signals — with semi-supervised techniques that extract structure from unlabeled environment interactions. The goal is to improve sample efficiency and generalization when reward feedback is

2 nguồn2020
deep learning

Semi-supervised RoBERTa-based Classification

Semi-supervised RoBERTa-based classification combines a large pretrained RoBERTa language model with both a small labeled dataset and a larger pool of unlabeled text. By generating pseudo-labels or enforcing consistency on unlabeled examples, the method extracts supervisory signal from unannotated data, yielding strong

2 nguồn2019
deep learning

Semi-supervised Semantic Segmentation

Semi-supervised semantic segmentation trains pixel-level labeling models using a small set of fully labeled images combined with a much larger set of unlabeled images. Techniques such as pseudo-labeling and consistency regularization extract supervisory signal from unlabeled data, making it possible to achieve near-ful

2 nguồn2018
deep learning

Semi-supervised Sentence Embeddings

Semi-supervised sentence embeddings combine a small set of labeled sentence pairs with large quantities of unlabeled text to train dense vector representations of sentences. By exploiting abundant unlabeled data through contrastive objectives or pseudo-labeling, these models produce high-quality embeddings for semantic

2 nguồn2019
deep learning

Semi-supervised Sentiment Analysis

Semi-supervised sentiment analysis combines a small set of manually labeled text samples with a large pool of unlabeled text to train opinion classifiers. By propagating sentiment signals from labeled seeds to unlabeled data through self-training, label propagation, or consistency regularization, the approach achieves

2 nguồn2002
machine learning

Semi-supervised Stacking Ensemble

Semi-supervised Stacking Ensemble extends the classic stacked generalization framework to settings where only a fraction of training examples carry labels. Base learners are first trained on labeled data, then used to assign pseudo-labels to unlabeled examples; the expanded dataset trains stronger base models whose out

2 nguồn2000
machine learning

Semi-supervised Support Vector Machine

Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution,

2 nguồn1999
deep learning

Semi-supervised Text Summarization

Semi-supervised text summarization trains summarization models by leveraging large amounts of unlabeled text alongside a small set of human-written reference summaries. By using techniques such as language-model pretraining, pseudo-labeling, and self-training, these methods substantially reduce the annotation burden wh

2 nguồn2018
deep learning

Semi-supervised Topic Modeling

Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for s

2 nguồn2009
machine learning

Semi-supervised Transfer Learning

Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive.

2 nguồn2010
deep learning

Semi-supervised Transformer

Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token pred

2 nguồn2018
deep learning

Semi-supervised Variational Autoencoder

The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarc

2 nguồn2014
deep learning

Semi-supervised Vision Transformer

Semi-supervised Vision Transformer applies the patch-based self-attention architecture of ViT to settings where only a fraction of images are labeled, exploiting large unlabeled corpora through pseudo-labeling, consistency regularization, or self-supervised pretext tasks before fine-tuning on the small labeled set. Thi

2 nguồn2021
machine learning

Semi-supervised Voting Ensemble

A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised lear

2 nguồn1998
deep learning

Semi-supervised Word2Vec

Semi-supervised Word2Vec trains dense word representations on a large unlabeled corpus using Word2Vec (skip-gram or CBOW), then uses those embeddings as fixed or fine-tunable input features for a downstream classifier trained on a small labeled dataset. This two-stage process lets models benefit from abundant unlabeled

2 nguồn2013
machine learning

Semi-supervised XGBoost

Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization whe

2 nguồn2016
data fusion

Sensor Fusion

Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art revi

1 nguồn2013
deep learning

Sentence Embeddings

Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the mos

2 nguồn2015
text mining

Sentiment Analysis

Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentime

1 nguồn
bioinformatics

Sequence Alignment

Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis,

2 nguồn1970
← 1920 / 2621 →