Glossary
Searchable terminology from accessibility, web standards, and related fields.
80 results found in Machine Learning.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- A density-based clustering algorithm introduced by Ester, Kriegel, Sander, and Xu (1996) that groups data points located in dense neighbourhoods and labels sparse points as noise. Unlike k-means, DBSC…
- Data Mining (Knowledge Discovery, KDD, Knowledge Discovery in Databases)
- Data mining is the computational process of discovering patterns, rules, and relationships in large datasets, drawing on techniques from statistics, machine learning, and database systems. Common task…
- Dataset Bias (Training Data Bias, Data Representation Bias, Sampling Bias)
- A systematic skew in the composition of training data used to build machine learning models, resulting in models that perform well for overrepresented groups but poorly for underrepresented ones. In a…
- Decision Tree (Classification Tree, Regression Tree, C4.5, CART)
- A decision tree is a supervised machine-learning model that represents a classification or regression decision as a tree of yes/no tests on input features, with predictions at the leaves. Well-known a…
- Diffusion Model (Diffusion-based Generator, Denoising Diffusion Model)
- A diffusion model is a class of generative AI that learns to produce images or videos by iteratively denoising a random noise input, reversing a forward process that gradually adds noise to training d…
- Document Layout Analysis (DLA, page layout analysis)
- A computer-vision task that identifies and classifies the visual regions of a document page—headings, paragraphs, tables, figures, captions, lists, headers, and footers—typically using object-detectio…
- Domain Adaptation (Cross-Domain Transfer, UDA, Unsupervised Domain Adaptation)
- A machine learning technique that enables models trained on data from one domain (such as web interfaces) to perform well on a different but related domain (such as mobile app interfaces). Domain adap…
- Eigenfaces
- A computer vision technique for face recognition that uses Principal Component Analysis to represent faces as a linear combination of standardized face components (eigenvectors derived from a training…
- Element Detection (UI Element Detection, Widget Detection, Object Detection)
- The task of automatically identifying the locations and types of user interface components (such as buttons, text fields, images, and checkboxes) from a screenshot using computer vision models. Elemen…
- Explainable AI (XAI, Interpretable AI)
- A set of methods and design approaches that make the outputs and decision-making processes of artificial intelligence systems understandable to human users. Explainable AI aims to provide transparency…
- Federated Learning (FL)
- A machine-learning approach in which a shared model is trained across many user devices without the raw training data ever leaving those devices: each device computes updates locally and sends only mo…
- Few-Shot Object Recognition (Few-Shot Recognition)
- A machine learning approach in which a model learns to identify a novel object from only a handful of labelled examples (commonly one to ten) rather than the hundreds or thousands typical of conventio…
- Fine-tuning (Model Fine-tuning, Fine-tune, Supervised Fine-tuning, SFT)
- A machine-learning technique that adapts a pre-trained foundation model - typically a large language model or vision model - to a specific task, domain, or individual user by continuing training on a …
- Gaussian Mixture Model (GMM)
- A Gaussian Mixture Model (GMM) is a probabilistic model that represents data as a weighted combination of multiple Gaussian (normal) distributions. Each component Gaussian has its own mean and covaria…
- Grad-CAM (Gradient-weighted Class Activation Mapping)
- A widely used explainable AI technique, introduced by Selvaraju et al. in 2017, that produces a class-discriminative heat map over an input image by weighting convolutional feature maps by the gradien…
- Inception-v3 (Inception v3)
- A deep convolutional neural network architecture developed by Google for image recognition, introduced in 2015. It uses "inception modules" that apply multiple convolution filter sizes in parallel to …
- LIME (Local Interpretable Model-agnostic Explanations)
- An explainable AI technique, introduced by Ribeiro et al. in 2016, that approximates any black-box model's behaviour around a single prediction by fitting a simple interpretable model (usually sparse …
- LLM-as-Judge (LLM as a Judge, Model-as-Judge)
- An evaluation methodology in which a large language model is prompted to assess the quality of some artifact — generated text, code, a UI, or a response from another model — according to a structured …
- LSTM (Long Short-Term Memory, LSTM Network)
- A type of recurrent neural network architecture designed to learn long-term dependencies in sequential data by using special gating mechanisms that control the flow of information through the network.…
- Large Vision Model (LVM)
- A large vision model is a foundation model trained on very large image (and often video) datasets to produce general-purpose visual representations - capable of object detection, segmentation, caption…