Supervised Learning
TechniqueSupervised learning is a machine-learning technique in which models such as classifiers are trained from labeled training data. In the provided evidence, it is used both as a general classifier-training paradigm with known security and privacy concerns, and as the basis for coverage-directed test selection in simulation-based verification.
First seen 5/30/2026
Last seen 6/3/2026
Evidence 2 chunks
Wiki v1
WIKI
Overview
Supervised learning is described in the provided evidence as a technique for training classifiers using labeled training data. One cited work notes that, given a pre-trained encoder used as a feature extractor, supervised learning can train a simple and accurate classifier with a small amount of labeled data.
Security and privacy considerations
NEIGHBORHOOD
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3 connections Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification ← uses 100% 4e
The paper's proposed method is based on supervised learning from coverage feedback.
The paper enhances constrained-random DV using supervised learning techniques.
Coverage-directed test selection is based on supervised learning from coverage feedback.
CITATIONS
7 sources7 citations — click to expand
[1] Supervised learning can train classifiers using labeled training data, and pre-trained encoders can be used as feature extractors to train simple accurate classifiers with small labeled datasets. Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
[2] Supervised-learning classifiers have security and privacy issues including data poisoning, backdoor attacks, adversarial examples, inference attacks, and right-to-be-forgotten concerns. Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
[3] Secure and privacy-preserving supervised-learning algorithms with formal guarantees may have limitations such as accuracy loss, small certified security guarantees, and inefficiency. Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
[4] Pre-trained encoders from self-supervised learning can improve accuracy, certified security guarantees, differentially private classifier accuracy, and exact machine-unlearning accuracy and/or efficiency in supervised-learning contexts. Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
[5] Constrained random test generation is widely used for simulation-based verification, but as verification progresses many constrained random tests have little or no effect on functional coverage. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification
[6] Coverage-directed test selection is based on supervised learning from coverage feedback and biases selection toward tests likely to increase functional coverage. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification
[7] Coverage-directed test selection can reduce manual constraint writing, prioritize effective tests, reduce verification resource consumption, and accelerate coverage closure on a large real-life industrial hardware design. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification