Definition
In statistical learning theory, a learnable function class represents the hypothesis space available to an algorithm. It defines the range of patterns or mappings the model can potentially capture based on its structure, such as linear models versus neural networks. The complexity of this class, often measured by VC dimension or Rademacher complexity, determines the model’s capacity to fit data and generalization ability, balancing bias and variance.
Summary
A learnable function class is a set of mathematical functions defined by a specific model architecture and parameter space that a learning algorithm can optimize.
Key Concepts
- Hypothesis space
- Model capacity
- VC dimension
Use Cases
- Selecting appropriate model architectures
- Theoretical analysis of generalization bounds
- Understanding model limitations