Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model

Keyphrases

• Machine Learning Models 100%
• Excess Risk 100%
• Agnostic 100%
• Exact Lower Bound 100%
• Probably Approximately Correct 100%
• Minimax 80%
• Expected Excess 80%
• Learning Algorithm 40%
• Asymptotic Lower Bound 40%
• Exact Asymptotics 40%
• Non-asymptotic 40%
• Voting Methods 40%
• Universal Constants 20%
• Machine Learning Classification 20%
• Binomial Distribution 20%
• Training Samples 20%
• Vapnik-Chervonenkis Dimension 20%
• Tail Probability 20%
• New Inequalities 20%
• New Identity 20%
• Asymptotic Bounds 20%
• Non-asymptotic Bound 20%
• Random Voting 20%
• Bayes Estimator 20%
• Empirical Risk 20%

Mathematics

• Excess Risk 100%
• Minimax 80%
• Asymptotics 60%
• Minimizes 20%
• Tail Probability 20%
• Simple Form 20%
• Vapnik-Chervonenkis Dimension 20%
• Binomial Distribution 20%
• Training Sample 20%
• Bayes Estimate 20%