Pedro Domingos
(13 articles)
Generalization
Ability of a ML model to perform well on new, unseen data that was not included in the training set.
Generality: 891
Supervision
Use of labeled data to train ML models, guiding the learning process by providing input-output pairs.
Generality: 890
Supervised Learning
ML approach where models are trained on labeled data to predict outcomes or classify data into categories.
Generality: 882
Inference
Process by which a trained neural network applies learned patterns to new, unseen data to make predictions or decisions.
Generality: 861
Curse of Dimensionality
Phenomenon where the complexity and computational cost of analyzing data increase exponentially with the number of dimensions or features.
Generality: 827
Learnability
Capacity of an algorithm or model to effectively learn from data, often measured by how well it can generalize from training data to unseen data.
Generality: 847
Continuous Learning
Systems and models that learn incrementally from a stream of data, updating their knowledge without forgetting previous information.
Generality: 870
Classifier
ML model that categorizes data into predefined classes.
Generality: 861
SRL
Statistical Relational Learning
Statistical Relational Learning
Combines statistics and relational data to construct models that can learn from complex, structured data involving multiple interdependent entities.
Generality: 500
Model-Based Classifier
ML algorithm that uses a pre-defined statistical model to make predictions based on input data.
Generality: 835
Black Box Problem
The difficulty in understanding and interpreting how an AI system, particularly ML models, makes decisions.
Generality: 850
Responsible AI
Application of AI in a manner that is transparent, unbiased, and respects user privacy and value.
Generality: 815
Hybrid AI
Combines symbolic AI (rule-based systems) and sub-symbolic AI (machine learning) approaches to leverage the strengths of both for more versatile and explainable AI systems.
Generality: 820