Extending Universal Approximation Guarantees: A Theoretical Justification for the Continuity of Real-World Learning Tasks.
Naveen DurvasulaPublished in: CoRR (2022)
Keyphrases
- learning tasks
- theoretical justification
- real world
- learning problems
- approximation guarantees
- learning algorithm
- machine learning
- supervised learning
- learning experience
- transfer learning
- multi task
- approximation algorithms
- machine learning algorithms
- greedy algorithm
- multi task learning
- multi label
- kernel methods
- training data
- data mining
- upper bound
- np hard
- special case
- lower bound
- image segmentation
- decision trees