Pages that link to "Item:Q5145841"
From MaRDI portal
The following pages link to A Survey on the Explainability of Supervised Machine Learning (Q5145841):
Displaying 21 items.
- Explicative deep learning with probabilistic formal concepts in a natural language processing task (Q1789735) (← links)
- Beneficial and harmful explanatory machine learning (Q2051274) (← links)
- Knowledge graphs as tools for explainable machine learning: a survey (Q2060751) (← links)
- Assessment of the influence of features on a classification problem: an application to COVID-19 patients (Q2077933) (← links)
- Explainable models of credit losses (Q2140185) (← links)
- Explanation in artificial intelligence: insights from the social sciences (Q2321252) (← links)
- How to explain individual classification decisions (Q2896115) (← links)
- Definitions, methods, and applications in interpretable machine learning (Q5218493) (← links)
- Witnesses for Answer Sets of Logic Programs (Q5886522) (← links)
- The explanation game: a formal framework for interpretable machine learning (Q6067308) (← links)
- Explainable subgradient tree boosting for prescriptive analytics in operations management (Q6087515) (← links)
- Risk-aware shielding of partially observable Monte Carlo planning policies (Q6088298) (← links)
- Explainable generalized additive neural networks with independent neural network training (Q6089210) (← links)
- Explaining black-box classifiers: properties and functions (Q6099552) (← links)
- A general framework for personalising post hoc explanations through user knowledge integration (Q6137842) (← links)
- Considerations when learning additive explanations for black-box models (Q6176233) (← links)
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy (Q6185714) (← links)
- Explainable ensemble trees (Q6538404) (← links)
- Explaining and predicting customer churn by monotonic rules induced from ordinal data (Q6572882) (← links)
- Synergies between machine learning and reasoning -- an introduction by the Kay R. Amel group (Q6577680) (← links)
- Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniques (Q6659088) (← links)
