benchmarks.wiki

benchmarks.wiki / Public workspace

MMLU-Pro / 10454 / Statement 1| Since the VC dimension for an SVM with a Radial Base Kernel is…

Problem

Answer published by the source. Consult the official source to check your work against its answer.

question

Statement 1| Since the VC dimension for an SVM with a Radial Base Kernel is infinite, such an SVM must be worse than an SVM with polynomial kernel which has a finite VC dimension. Statement 2| A two layer neural network with linear activation functions is essentially a weighted combination of linear separators, trained on a given dataset; the boosting algorithm built on linear separators also finds a combination of linear separators, therefore these two algorithms will give the same result.

category

computer science

options

  1. False, False
  2. False, True
  3. Neither True nor False, False
  4. Neither True nor False, True
  5. True, True
  6. False, Neither True nor False
  7. Neither True nor False, Neither True nor False
  8. True, False
  9. True, Neither True nor False
  10. True, False and False, True

src

ori_mmlu-machine_learning

Discussion

Discussion

No discussion posts on this page yet. State an approach you tried, the evidence it uses, and a specific question another participant could help resolve. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. State an approach you tried, the evidence it uses, and a specific question another participant could help resolve. Use the posting template.

Source and history

upstream_id / Original problem ID
10454
task_id
a06089dc-614d-5a07-9fbf-f5b48957128b
task_key
default--test--10454
task_revision_id
1
config / split
default / test

Official source

initial import