日本語索引にほんごさくいん

かたから用語ようごさがせます。

アクタークリティック (Wikipedia)
Actor–critic
A method combining an actor that updates the policy with a critic that evaluates the actor using value estimates.
Used on 1 topic page: Reinforcement Learning
アルゴリズム (Wikipedia)
Algorithm
A defined procedure used to solve a problem or perform a computation.
Used on 1 topic page: Supervised Learning
アルゴリズム影響評価えいきょうひょうか (Wikipedia)
Algorithmic impact assessment
A structured assessment of an automated system's expected effects, risks, and safeguards.
Used on 1 topic page: AI Regulation and Policy
アルゴリズムかたよ (Wikipedia)
Algorithmic bias
Systematic and unfair differences in algorithmic outcomes affecting people or groups.
Used on 1 topic page: AI Ethics
異常いじょう検知けんち (Wikipedia)
Anomaly detection
Identifying unusual patterns or outliers in data.
Used on 1 topic page: Unsupervised Learning
一般化いっぱんか (Wikipedia)
Generalization
A model’s ability to perform well on data it did not see during training.
Used on 1 topic page: Supervised Learning
意味的類似度いみてきるいじど (Wikipedia)
Semantic similarity
A measure of how closely two items match in meaning rather than surface form.
Used on 1 topic page: Embeddings and Representation Learning
医用画像いようがぞう (Wikipedia)
Medical imaging
Techniques for creating and interpreting images of the body for clinical purposes.
Used on 1 topic page: AI in Healthcare
医療分野いりょうぶんやのAI (Wikipedia)
AI in healthcare
The application of AI to clinical care, medical operations, research, and public health.
Used on 1 topic page: AI in Healthcare
(Wikipedia)
Embedding
A learned numerical representation that places items in a vector space.
Used on 1 topic page: Embeddings and Representation Learning
エピソード (Wikipedia)
Episode
One complete sequence of interaction from an initial state until a terminal state.
Used on 1 topic page: Reinforcement Learning
AIエージェント (Wikipedia)
AI agent
An AI system that observes context, chooses actions, and works toward a goal.
Used on 1 topic page: AI Agents and Tool Use
AIガバナンス (Wikipedia)
AI governance
The structures, responsibilities, and processes used to direct and oversee AI systems.
Used on 1 topic page: AI Regulation and Policy
AI規制きせい政策せいさく (Wikipedia)
AI regulation and policy
Rules and public policies governing the development, deployment, and use of AI.
Used on 1 topic page: AI Regulation and Policy
AIの安全性あんぜんせい評価ひょうか (Wikipedia)
AI safety and evaluation
The practice of measuring AI capabilities, limitations, and risks and reducing the likelihood or severity of harmful outcomes.
Used on 1 topic page: AI Safety and Evaluation
AI倫理りんり (Wikipedia)
AI ethics
The study and practice of addressing moral issues arising from AI design, deployment, and use.
Used on 1 topic page: AI Ethics
エージェント (Wikipedia)
Agent
The decision-making entity that observes the environment, chooses actions, and receives rewards.
Used on 1 topic page: Reinforcement Learning

回帰かいき (Wikipedia)
Regression
A supervised-learning task that estimates a continuous numerical target.
Used on 1 topic page: Regression
解釈可能性かいしゃくかのうせい (Wikipedia)
Interpretability
The degree to which a person can understand how a model produces an output.
Used on 1 topic page: Explainable AI (XAI)
確率分布かくりつぶんぷ (Wikipedia)
Probability distribution
A mathematical description of the probabilities assigned to possible outcomes.
Used on 1 topic page: Probabilistic Models
確率変数かくりつへんすう (Wikipedia)
Random variable
A variable whose possible numerical values are outcomes of a random process.
Used on 1 topic page: Probabilistic Models
確率かくりつモデル (Wikipedia)
Probabilistic model
A model that represents variables and outcomes using probability distributions.
Used on 1 topic page: Probabilistic Models
かくそう (Wikipedia)
Hidden layer
A neural-network layer located between the input and output layers.
Used on 1 topic page: Deep Learning
価値関数かちかんすう (Wikipedia)
Value function
A function that estimates the expected return from a state, or from taking an action in a state, under a policy.
Used on 1 topic page: Reinforcement Learning
価値かちベース手法しゅほう (Wikipedia)
Value-based method
A method that learns value estimates and derives action choices from them.
Used on 1 topic page: Reinforcement Learning
活性化関数かっせいかかんすう (Wikipedia)
Activation function
A function that transforms a neuron's combined input and introduces nonlinear behavior.
Used on 1 topic page: Neural Networks
活用かつよう (Wikipedia)
Exploitation
Choosing actions currently believed to produce the highest return.
Used on 1 topic page: Reinforcement Learning
下流かりゅうタスク (Wikipedia)
Downstream task
A specific application for which a pretrained model is adapted or evaluated.
Used on 1 topic page: Foundation Models
環境かんきょう (Wikipedia)
Environment
Everything outside the agent that responds to its actions and supplies states and rewards.
Used on 1 topic page: Reinforcement Learning
画像がぞうセグメンテーション (Wikipedia)
Image segmentation
The task of assigning image pixels to meaningful regions or classes.
Used on 1 topic page: Computer Vision
画像がぞう分類ぶんるい (Wikipedia)
Image classification
Assigning a label or category to an image.
Used on 1 topic page: Supervised Learning
頑健性がんけんせい (Wikipedia)
Robustness
The ability of a system to maintain acceptable behavior under variation, noise, or attack.
Used on 1 topic page: AI Safety and Evaluation
機械きかい学習がくしゅう (Wikipedia)
Machine learning
A field of AI in which systems learn patterns or behavior from data and experience.
Used on 4 topic pages: Artificial Intelligence · Machine Learning · Reinforcement Learning · Supervised Learning
基盤きばんモデル (Wikipedia)
Foundation model
A model trained broadly at scale that can be adapted to many downstream tasks.
Used on 1 topic page: Foundation Models
Q学習がくしゅう (Wikipedia)
Q-learning
A model-free, value-based algorithm that learns the expected return for taking each action in each state.
Used on 1 topic page: Reinforcement Learning
強化学習きょうかがくしゅう (Wikipedia)
Reinforcement learning
A machine-learning approach in which an agent learns behavior through interaction and feedback from rewards.
Used on 1 topic page: Reinforcement Learning
教師きょうしあり学習がくしゅう (Wikipedia)
Supervised learning
A machine-learning approach in which a model is trained on labeled examples.
Used on 1 topic page: Supervised Learning
教師きょうしなし学習がくしゅう (Wikipedia)
Unsupervised learning
A machine-learning approach in which a model learns patterns from unlabeled data.
Used on 1 topic page: Unsupervised Learning
金融分野きんゆうぶんやのAI (Wikipedia)
AI in finance
The application of AI to financial analysis, decisions, services, and risk management.
Used on 1 topic page: AI in Finance
クラスタリング (Wikipedia)
Clustering
Grouping similar data points according to their features.
Used on 1 topic page: Unsupervised Learning
クラスラベル (Wikipedia)
Class label
The category name or identifier predicted in a classification task.
Used on 1 topic page: Classification
訓練くんれん (Wikipedia)
Training
The process of adjusting a model’s parameters using data.
Used on 1 topic page: Supervised Learning
訓練くんれんデータ (Wikipedia)
Training data
Examples used to fit a model's parameters or learned behavior.
Used on 2 topic pages: Machine Learning · Training, Validation, and Testing
訓練くんれん検証けんしょう・テストデータ (Wikipedia)
Training, validation, and test data
Separate data subsets used to fit a model, tune choices, and estimate final performance.
Used on 1 topic page: Training, Validation, and Testing
グラウンディング (Wikipedia)
Grounding
Connecting a model's output to supplied evidence, data, or observable context.
Used on 1 topic page: Retrieval-Augmented Generation (RAG)
グラフ (Wikipedia)
Graph
A structure consisting of entities represented as vertices and relationships represented as edges.
Used on 1 topic page: Graph Neural Networks (GNNs)
グラフニューラルネットワーク (Wikipedia)
Graph neural network
A neural network designed to learn from nodes, edges, and relationships in graph-structured data.
Used on 1 topic page: Graph Neural Networks (GNNs)
計画けいかく (Wikipedia)
Planning
Selecting and ordering actions intended to move from a current state toward a goal.
Used on 1 topic page: AI Agents and Tool Use
検索拡張生成けんさくかくちょうせいせい (Wikipedia)
Retrieval-augmented generation
A generation method that retrieves external information and supplies it to a generative model as context.
Used on 1 topic page: Retrieval-Augmented Generation (RAG)
検証けんしょうデータ (Wikipedia)
Validation data
Data used during development to compare settings or select models without fitting their parameters directly.
Used on 1 topic page: Training, Validation, and Testing
言語げんごモデル (Wikipedia)
Language model
A model that assigns probabilities to sequences of language elements or predicts language content.
Used on 1 topic page: Large Language Models
交差検証こうさけんしょう (Wikipedia)
Cross-validation
A resampling method that evaluates a model across multiple training and validation partitions.
Used on 1 topic page: Model Evaluation
行動こうどう (Wikipedia)
Action
A choice made by the agent that can affect the environment and future rewards.
Used on 1 topic page: Reinforcement Learning
勾配降下法こうばいこうかほう (Wikipedia)
Gradient descent
An iterative optimization method that moves parameters in the direction that reduces an objective.
Used on 1 topic page: Optimization Algorithms
公平性こうへいせい (Wikipedia)
Fairness
The study and management of unjustified differences in how AI systems affect people or groups.
Used on 1 topic page: AI Safety and Evaluation
固有表現抽出こゆうひょうげんちゅうしゅつ (Wikipedia)
Named-entity recognition
The task of locating and categorizing names such as people, organizations, and places in text.
Used on 1 topic page: Natural Language Processing (NLP)
コンテキストウィンドウ (Wikipedia)
Context window
The amount of input and generated content a model can consider in one interaction.
Used on 1 topic page: Large Language Models
コンピュータビジョン (Wikipedia)
Computer vision
The field that enables computers to extract information from images and video.
Used on 1 topic page: Computer Vision
誤差逆伝播法ごさぎゃくでんぱほう (Wikipedia)
Backpropagation
An algorithm that computes gradients by propagating output error backward through a neural network.
Used on 1 topic page: Deep Learning

最適化さいてきかアルゴリズム (Wikipedia)
Optimization algorithm
A procedure for finding parameter values that improve or minimize an objective.
Used on 1 topic page: Optimization Algorithms
サポートセット (Wikipedia)
Support set
The small labeled example set supplied for adaptation in a few-shot learning task.
Used on 1 topic page: Meta-Learning
サンプリング (Wikipedia)
Sampling
The process of drawing examples from a probability distribution or generative model.
Used on 1 topic page: Generative Models
視覚言語しかくげんごモデル (Wikipedia)
Vision-language model
A model trained to represent and reason across visual and linguistic information.
Used on 1 topic page: Multimodal AI
市場しじょうセグメンテーション (Wikipedia)
Market segmentation
Dividing a market into groups with shared characteristics or behavior.
Used on 1 topic page: Unsupervised Learning
自然言語処理しぜんげんごしょり (Wikipedia)
Natural language processing
The field concerned with computational processing and generation of human language.
Used on 1 topic page: Natural Language Processing (NLP)
収益しゅうえき (Wikipedia)
Return
The total reward accumulated from a time step onward, usually with future rewards discounted.
Used on 1 topic page: Reinforcement Learning
終端状態しゅうたんじょうたい (Wikipedia)
Terminal state
A state that ends an episode and after which no further action is taken in that episode.
Used on 1 topic page: Reinforcement Learning
出力しゅつりょくラベル (Wikipedia)
Output label
The expected category or value associated with an input.
Used on 1 topic page: Supervised Learning
少数しょうすうショット学習がくしゅう (Wikipedia)
Few-shot learning
Learning or adapting from only a small number of labeled examples.
Used on 1 topic page: Meta-Learning
深層強化学習しんそうきょうかがくしゅう (Wikipedia)
Deep reinforcement learning
Reinforcement learning that uses deep neural networks to represent policies, value functions, or environment models.
Used on 1 topic page: Reinforcement Learning
信用しんようスコアリング (Wikipedia)
Credit scoring
Estimating the likelihood that a borrower will repay debt.
Used on 1 topic page: AI in Finance
時間じかんステップ (Wikipedia)
Time step
One discrete point in an interaction sequence at which the agent observes, acts, and receives feedback.
Used on 1 topic page: Reinforcement Learning
次元じげん削減さくげん (Wikipedia)
Dimensionality reduction
Reducing the number of features while retaining essential structure or information.
Used on 1 topic page: Unsupervised Learning
自己位置推定じこいちすいてい (Wikipedia)
Localization
Estimating the position and orientation of a robot or autonomous system.
Used on 1 topic page: Autonomous Systems
自己教師じこきょうしあり学習がくしゅう (Wikipedia)
Self-supervised learning
Learning in which supervisory signals are derived from the structure of the input data itself.
Used on 2 topic pages: Foundation Models · Self-Supervised Learning
自己注意機構じこちゅういきこう (Wikipedia)
Self-attention
Attention in which elements of one sequence attend to other elements in that same sequence.
Used on 1 topic page: Transformers and Attention
事後確率じごかくりつ (Wikipedia)
Posterior probability
A probability updated after combining prior belief with observed evidence.
Used on 1 topic page: Bayesian Learning
事前確率じぜんかくりつ (Wikipedia)
Prior probability
A probability expressing belief before incorporating the current evidence.
Used on 1 topic page: Bayesian Learning
事前学習じぜんがくしゅう (Wikipedia)
Pretraining
Initial training on broad data before a model is adapted for a more specific purpose.
Used on 1 topic page: Foundation Models
事前学習済じぜんがくしゅうずみモデル (Wikipedia)
Pretrained model
A model whose parameters were learned before adaptation to the current task.
Used on 1 topic page: Transfer Learning
自動化じどうかバイアス (Wikipedia)
Automation bias
The tendency to favor suggestions from automated systems even when contrary evidence exists.
Used on 1 topic page: Human–AI Interaction
状態じょうたい (Wikipedia)
State
A representation of the information needed to describe the environment at a particular time.
Used on 1 topic page: Reinforcement Learning
状態遷移じょうたいせんい (Wikipedia)
State transition
The change from one state to another after the agent takes an action.
Used on 1 topic page: Reinforcement Learning
情報検索じょうほうけんさく (Wikipedia)
Information retrieval
Finding relevant documents or records in response to an information need.
Used on 1 topic page: Retrieval-Augmented Generation (RAG)
自律じりつシステム (Wikipedia)
Autonomous system
A system that can operate and make decisions with limited direct human control.
Used on 2 topic pages: Artificial Intelligence · Autonomous Systems
人工知能じんこうちのう (Wikipedia)
Artificial intelligence
The field concerned with building machines that perform tasks associated with human intelligence.
Used on 2 topic pages: Artificial Intelligence · Machine Learning
人工じんこうニューロン (Wikipedia)
Artificial neuron
A mathematical unit that combines inputs and applies an activation function to produce an output.
Used on 1 topic page: Neural Networks
推論すいろん (Wikipedia)
Inference
Using a trained model to produce an output from new input.
Used on 1 topic page: Model Deployment and Monitoring
スパムメール検出けんしゅつ (Wikipedia)
Spam email detection
Classifying email as spam or not spam.
Used on 1 topic page: Supervised Learning
生成せいせいモデル (Wikipedia)
Generative model
A model that learns a data distribution and can produce new samples resembling its training data.
Used on 1 topic page: Generative Models
説明可能せつめいかのうなAI (Wikipedia)
Explainable AI
Methods and practices intended to make AI behavior understandable to people.
Used on 1 topic page: Explainable AI (XAI)
説明責任せつめいせきにん (Wikipedia)
Accountability
Responsibility for decisions, impacts, oversight, and remedies associated with an AI system.
Used on 1 topic page: AI Ethics
センサフュージョン (Wikipedia)
Sensor fusion
Combining measurements from multiple sensors to obtain a more reliable estimate.
Used on 1 topic page: Autonomous Systems
潜在変数せんざいへんすう (Wikipedia)
Latent variable
An unobserved variable used by a model to explain patterns in observed data.
Used on 1 topic page: Generative Models
損失そんしつ関数かんすう (Wikipedia)
Loss function
A formula that measures the difference between predicted and actual outputs.
Used on 1 topic page: Supervised Learning

対照学習たいしょうがくしゅう (Wikipedia)
Contrastive learning
Learning representations by bringing related examples closer and separating unrelated examples.
Used on 1 topic page: Self-Supervised Learning
対象たいしょうタスク (Wikipedia)
Target task
The task to which transferred knowledge or a pretrained model is being applied.
Used on 1 topic page: Transfer Learning
クラス分類ぶんるい (Wikipedia)
Multiclass classification
Classification in which an example is selected from more than two possible classes.
Used on 1 topic page: Classification
探索たんさく (Wikipedia)
Exploration
Trying actions whose outcomes are uncertain in order to discover potentially better behavior.
Used on 1 topic page: Reinforcement Learning
大規模言語だいきぼげんごモデル (Wikipedia)
Large language model
A language model with many parameters trained on large text collections for broad language capabilities.
Used on 1 topic page: Large Language Models
代理だいりタスク (Wikipedia)
Pretext task
A task constructed from unlabeled data to provide a learning signal for representation learning.
Used on 1 topic page: Self-Supervised Learning
知的ちてきエージェント (Wikipedia)
Intelligent agent
A system that perceives an environment and acts to pursue objectives.
Used on 1 topic page: Artificial Intelligence
注意機構ちゅういきこう (Wikipedia)
Attention
A mechanism that assigns different weights to input elements when computing a representation.
Used on 1 topic page: Transformers and Attention
ツール使用しよう (Wikipedia)
Tool use
The ability of an AI system to invoke external functions, software, or services to complete work.
Used on 1 topic page: AI Agents and Tool Use
敵対的機械学習てきたいてききかいがくしゅう (Wikipedia)
Adversarial machine learning
The study of attacks on machine-learning systems and defenses against them.
Used on 1 topic page: Adversarial Machine Learning
敵対的てきたいてきサンプル (Wikipedia)
Adversarial example
An input deliberately modified to cause a model to make an incorrect prediction.
Used on 1 topic page: Adversarial Machine Learning
テストデータ (Wikipedia)
Test data
Held-out data used to estimate a finished model's performance on unseen examples.
Used on 1 topic page: Training, Validation, and Testing
転移学習てんいがくしゅう (Wikipedia)
Transfer learning
Reusing knowledge learned for one task or domain to improve learning on another.
Used on 1 topic page: Transfer Learning
ディープラーニング (Wikipedia)
Deep learning
Machine learning based on neural networks with multiple learned representation layers.
Used on 1 topic page: Deep Learning
データ (Wikipedia)
Data
Recorded observations, measurements, symbols, or examples used for analysis and learning.
Used on 1 topic page: Data and Datasets
データ可視化かしか (Wikipedia)
Data visualization
Representing data graphically to reveal patterns, trends, and insights.
Used on 1 topic page: Unsupervised Learning
データセット (Wikipedia)
Dataset
An organized collection of related data used for analysis or model development.
Used on 1 topic page: Data and Datasets
データとデータセット (Wikipedia)
Data and datasets
The examples and organized collections used for analysis and machine learning.
Used on 1 topic page: Data and Datasets
データドリフト (Wikipedia)
Data drift
A change over time in the statistical properties of data received by a deployed system.
Used on 1 topic page: Model Deployment and Monitoring
データ分割ぶんかつ (Wikipedia)
Data split
The process of partitioning a dataset into distinct subsets for model development and evaluation.
Used on 1 topic page: Training, Validation, and Testing
データポイズニング (Wikipedia)
Data poisoning
An attack that corrupts training data to influence a model's learned behavior.
Used on 1 topic page: Adversarial Machine Learning
データリーク (Wikipedia)
Data leakage
The unintended use of information during training that would not be available when the model is deployed.
Used on 1 topic page: Training, Validation, and Testing
特徴選択とくちょうせんたく (Wikipedia)
Feature selection
Selecting a useful subset of available input features.
Used on 1 topic page: Feature Engineering
特徴抽出とくちょうちゅうしゅつ (Wikipedia)
Feature extraction
Transforming raw data into informative derived features.
Used on 1 topic page: Feature Engineering
特徴量とくちょうりょう (Wikipedia)
Feature
A measurable input property supplied to a machine-learning model.
Used on 1 topic page: Data and Datasets
特徴量とくちょうりょうエンジニアリング (Wikipedia)
Feature engineering
The process of creating or transforming model inputs to improve learning and performance.
Used on 1 topic page: Feature Engineering
特徴量重要度とくちょうりょうじゅうようど (Wikipedia)
Feature importance
A measure of how strongly an input feature influences a model's predictions.
Used on 1 topic page: Explainable AI (XAI)
トランスフォーマー (Wikipedia)
Transformer
A neural-network architecture that processes relationships between tokens primarily through attention mechanisms.
Used on 2 topic pages: Large Language Models · Transformers and Attention
トークン (Wikipedia)
Token
A unit of text or other input that a model processes as one element of a sequence.
Used on 2 topic pages: Large Language Models · Transformers and Attention
トークン (Wikipedia)
Tokenization
The process of dividing input into units that a language-processing system can handle.
Used on 1 topic page: Natural Language Processing (NLP)
動作計画どうさけいかく (Wikipedia)
Motion planning
Computing a feasible sequence of movements from a starting configuration to a goal.
Used on 1 topic page: AI in Robotics

二値分類にちぶんるい (Wikipedia)
Binary classification
Classification in which each example is assigned to one of two classes.
Used on 1 topic page: Classification
入力にゅうりょくデータ (Wikipedia)
Input data
The raw information or features supplied to a model.
Used on 1 topic page: Supervised Learning
ニューラルネットワーク (Wikipedia)
Neural network
A model composed of connected processing units arranged in layers.
Used on 1 topic page: Neural Networks
人間にんげんとAIの相互作用そうごさよう (Wikipedia)
Human–AI interaction
The study and design of communication, cooperation, and control between people and AI systems.
Used on 1 topic page: Human–AI Interaction

ハルシネーション (Wikipedia)
Hallucination
Generated content that is unsupported, false, or inconsistent with the supplied evidence.
Used on 1 topic page: AI Safety and Evaluation
ヒューマン・イン・ザ・ループ (Wikipedia)
Human in the loop
A design in which people provide oversight, decisions, labels, or feedback within an automated process.
Used on 1 topic page: Human–AI Interaction
評価指標ひょうかしひょう (Wikipedia)
Evaluation metric
A numerical measure used to assess a model's predictions or behavior.
Used on 1 topic page: Model Evaluation
表現学習ひょうげんがくしゅう (Wikipedia)
Representation learning
Learning useful features or representations directly from data.
Used on 1 topic page: Embeddings and Representation Learning
ファインチューニング (Wikipedia)
Fine-tuning
Further training a pretrained model on task-specific data or objectives.
Used on 1 topic page: Foundation Models
不正検知ふせいけんち (Wikipedia)
Fraud detection
Identifying transactions or behavior that may involve deception or unauthorized activity.
Used on 1 topic page: AI in Finance
物体検出ぶったいけんしゅつ (Wikipedia)
Object detection
The task of locating and classifying objects within an image or video.
Used on 1 topic page: Computer Vision
分類ぶんるい (Wikipedia)
Classification
The task of assigning an input to one or more discrete categories.
Used on 1 topic page: Classification
ベイズ学習がくしゅう (Wikipedia)
Bayesian learning
Learning that applies Bayes' theorem to update uncertainty about models or parameters.
Used on 1 topic page: Bayesian Learning
ベクトル (Wikipedia)
Vector
An ordered collection of numbers used to represent position, direction, or learned features.
Used on 1 topic page: Embeddings and Representation Learning
ベクトルデータベース (Wikipedia)
Vector database
A database designed to store embeddings and retrieve nearby vectors efficiently.
Used on 1 topic page: Retrieval-Augmented Generation (RAG)
ベンチマーク (Wikipedia)
Benchmark
A standardized task, dataset, or measurement used to compare systems.
Used on 1 topic page: AI Safety and Evaluation
方策ほうさく (Wikipedia)
Policy
A rule or probability distribution that determines which action an agent selects in each state.
Used on 1 topic page: Reinforcement Learning
方策勾配ほうさくこうばい (Wikipedia)
Policy gradient
A family of policy-based methods that improves a policy by following the gradient of expected return.
Used on 1 topic page: Reinforcement Learning
方策ほうさくベース手法しゅほう (Wikipedia)
Policy-based method
A method that optimizes a policy directly instead of deriving it only from learned values.
Used on 1 topic page: Reinforcement Learning
報酬ほうしゅう (Wikipedia)
Reward
A numerical feedback signal that indicates the immediate desirability of an outcome.
Used on 1 topic page: Reinforcement Learning

マルコフ決定過程けっていかてい (Wikipedia)
Markov Decision Process (MDP)
A mathematical framework for sequential decisions defined by states, actions, transition probabilities, rewards, and a discount factor.
Used on 1 topic page: Reinforcement Learning
マルチモーダル (Wikipedia)
Multimodal embedding
A representation that places information from different modalities in a shared or aligned vector space.
Used on 1 topic page: Multimodal AI
マルチモーダルAI (Wikipedia)
Multimodal AI
AI that processes or combines more than one kind of data, such as text, images, audio, or video.
Used on 1 topic page: Multimodal AI
未見みけんデータ (Wikipedia)
Unseen data
Data that a model did not encounter during training.
Used on 1 topic page: Supervised Learning
メタラーニング (Wikipedia)
Meta-learning
Learning methods that improve a system's ability to learn new tasks from limited experience.
Used on 1 topic page: Meta-Learning
メッセージパッシング (Wikipedia)
Message passing
A graph-learning procedure in which nodes update their representations using information from neighboring nodes.
Used on 1 topic page: Graph Neural Networks (GNNs)
メモリ (Wikipedia)
Memory
Stored information that an agent can retain and use across steps or interactions.
Used on 1 topic page: AI Agents and Tool Use
目的関数もくてきかんすう (Wikipedia)
Objective function
A numerical function that an optimization process seeks to minimize or maximize.
Used on 1 topic page: Optimization Algorithms
目的変数もくてきへんすう (Wikipedia)
Target variable
The outcome that a supervised model is trained to predict.
Used on 1 topic page: Regression
目標もくひょう (Wikipedia)
Target value
The expected output used in supervised learning and absent from unsupervised training data.
Used on 1 topic page: Unsupervised Learning
モダリティ (Wikipedia)
Modality
A particular form or channel of information, such as text, vision, or audio.
Used on 1 topic page: Multimodal AI
モデル (Wikipedia)
Model
A mathematical representation that processes input data to produce predictions, classifications, or decisions.
Used on 4 topic pages: Artificial Intelligence · Machine Learning · Supervised Learning · Unsupervised Learning
モデル監視かんし (Wikipedia)
Model monitoring
Tracking a deployed model's behavior, performance, inputs, and operational health.
Used on 1 topic page: Model Deployment and Monitoring
モデル集約しゅうやく (Wikipedia)
Model aggregation
The process of combining model updates received from multiple federated clients.
Used on 1 topic page: Federated Learning
モデル展開てんかい (Wikipedia)
Model deployment
Making a trained model available in a production system or application.
Used on 1 topic page: Model Deployment and Monitoring
モデル評価ひょうか (Wikipedia)
Model evaluation
The systematic measurement of how well a model performs for its intended purpose.
Used on 1 topic page: Model Evaluation
モデルフリー強化学習きょうかがくしゅう (Wikipedia)
Model-free reinforcement learning
Reinforcement learning that learns values or policies directly from experience without learning a model for planning.
Used on 1 topic page: Reinforcement Learning
モデルベース強化学習きょうかがくしゅう (Wikipedia)
Model-based reinforcement learning
Reinforcement learning that uses a learned or supplied model of environment dynamics to plan or improve behavior.
Used on 1 topic page: Reinforcement Learning

予測よそく (Wikipedia)
Prediction
An output produced by a trained model.
Used on 1 topic page: Supervised Learning
予測よそく誤差ごさ (Wikipedia)
Prediction error
The difference between a predicted value and its target value.
Used on 1 topic page: Supervised Learning

ラベル (Wikipedia)
Label
A known category or target attached to an example in labeled data.
Used on 1 topic page: Data and Datasets
ラベルきデータセット (Wikipedia)
Labeled dataset
A collection in which each input is paired with its expected output or target.
Used on 1 topic page: Supervised Learning
臨床意思決定支援りんしょういしけっていしえん (Wikipedia)
Clinical decision support
Software that supplies patient-specific information to assist healthcare decisions.
Used on 1 topic page: AI in Healthcare
レッドチーミング (Wikipedia)
Red teaming
Adversarial testing that searches deliberately for failures, vulnerabilities, or harmful behavior.
Used on 1 topic page: AI Safety and Evaluation
連合学習れんごうがくしゅう (Wikipedia)
Federated learning
Distributed learning in which participants train locally and share model updates rather than raw data.
Used on 1 topic page: Federated Learning
連合れんごうクライアント (Wikipedia)
Federated client
A participating device or organization that performs local training in federated learning.
Used on 1 topic page: Federated Learning
連続値れんぞくち (Wikipedia)
Continuous value
A numerical value that can vary across an interval rather than belonging to a discrete class.
Used on 1 topic page: Regression
ロボット知覚ちかく (Wikipedia)
Robot perception
The process by which a robot interprets sensor data to understand itself and its surroundings.
Used on 1 topic page: AI in Robotics
ロボティクス分野ぶんやのAI (Wikipedia)
AI in robotics
The use of AI for robot perception, decision-making, learning, and control.
Used on 1 topic page: AI in Robotics

割引率わりびきりつ (Wikipedia)
Discount factor
A value between zero and one that controls how strongly future rewards affect present decisions.
Used on 1 topic page: Reinforcement Learning