XOR Problem
Definition
The XOR problem shows the limits of a single linear decision boundary in classification tasks.
A classic classification problem whose positive and negative points cannot be separated by a single linear boundary.
Simply explained
Four points belong alternately to two groups. A single straight line cannot cleanly separate the two groups.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A training uses XOR to demonstrate the need for nonlinear features.
Limits and pitfalls
- XOR neither proves the superiority of deep networks nor a real business problem; architecture and activation are decisive.
Origin & history
Minsky and Papert showed in 1969 the limits of single-layer perceptrons; multilayer networks can represent XOR.
Use in mid-sized companies
- Possible application: A training uses XOR to demonstrate the need for nonlinear features.
Frequently asked questions
What does XOR problem mean?
A classic classification problem whose positive and negative points cannot be separated by a single linear boundary. What should you watch out for? XOR neither proves the superiority of deep networks nor a real business problem; architecture and activation are decisive.
Sources
- Learning representations by back-propagating errors · Rumelhart et al., Learning representations by back-propagating errors
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
Editorially reviewed on: