How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Indexed shattering and VC dimension of a binary class
Definition
Let be a nonempty measurable space and let be a nonempty class of measurable functions . For an integer and an indexed tuple , define its trace by
The tuple is shattered by if . Its coordinates are indexed: repeated instance points remain distinct coordinates, although their labels must agree for every . For , and , so the empty tuple is shattered. The VC dimension of is
Definition references
Shattering and the Vapnik–Chervonenkis dimension of a set family: its current definition treats a family ; on an indexed sample, identify each with the subset , keeping repeated sample points at separate indices.
Mohri et al., Definition 3.10: VC dimension of a hypothesis class.
Bartlett and Mendelson, Theorem 6: sample-restricted VC dimension.
Depends on
Used by
- Growth function of a binary hypothesis class Definition
- Affine halfspaces in real p-space have VC dimension p plus one Example
- An infinite singleton class has VC dimension one Example
- Intervals on the real line have VC dimension two Example
- Thresholds on the real line have VC dimension one Example
- VC entropy bound for empirical Rademacher complexity Lemma
- One-sided VC uniform convergence and the finite-class branch Theorem
- Realizable sample complexity for finite-VC classes Theorem
- Sauer–Shelah growth bound for indexed samples Theorem
Dependency tree · two levels
6 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, Foundations of Machine Learning, 2nd ed., Definition 3.10 (standard reference, not scraped)
- Peter L. Bartlett and Shahar Mendelson, Rademacher and Gaussian Complexities: Risk Bounds and Structural Results, Theorem 6 (standard reference, not scraped)