Generalization of the PAC-model for learning with partial information:(Extended abstract)

Computer Science

• Approximation (Algorithm) 100%
• Theoretical Framework 100%
• Linear Operator 100%
• Real-Valued Function 100%
• Learning Problem 100%
• Machine Learning 100%
• Learning System 100%

Mathematics

• Real Valued Function 100%
• Approximation Error 100%
• Lower and upper bounds 100%
• Information-Based Complexity 100%
• Chervonenkis 100%
• Partial Model 100%
• Linear Operator 100%

Keyphrases

• Partial Information 100%
• Extended Abstracts 100%
• PAC Model 100%
• Sample Complexity 33%
• Class Function 22%
• Machine Learning 11%
• Learning Model 11%
• Real-valued Functions 11%
• Pseudo-dimension 11%
• Learning Problems 11%
• Approximation Error 11%
• Tight Lower Bound 11%
• Tight Upper Bound 11%
• Optimal Way 11%
• Combined Information 11%
• Linear Operator 11%
• Vapnik-Chervonenkis Theory 11%
• Information Complexity 11%
• Minimal Approximation 11%
• Complexity Theory 11%
• Sobolev 11%
• Almost Optimal 11%