Pattern recognition and Valiant's learning framework

Pattern Recognition and Machine Learning

✍ Christopher M. Bishop 📂 Library 📅 2006 🏛 Springer 🌐 English ⚖ 5 MB

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models ha

Pattern Recognition and Machine Learning

✍ Christopher M. Bishop 📂 Library 📅 2006 🏛 Springer 🌐 English ⚖ 9 MB

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models ha

Quantifying inductive bias: AI learning

Quantifying inductive bias: AI learning algorithms and valiant's learning framework: D. Haussler [Artificial Intelligence 36 (1988) 177–221]

📂 Article 📅 1990 🏛 Elsevier Science 🌐 English ⚖ 20 KB

Theorem 5.1 was previously obtained by J. Hong in [1]. Hong also suggests that the greedy method for set cover be applied to obtain a small hypothesis for this and related learning problems. I thank him very much for bringing this to my attention, and apologize for not being aware of his work earlie

Interaction of learning and self-learnin

Interaction of learning and self-learning in pattern recognition

✍ M. I. Shlezinger 📂 Article 📅 1972 🏛 Springer US 🌐 English ⚖ 70 KB

Learning to Learn, Pattern Recognition,

Learning to Learn, Pattern Recognition, and Nash Equilibrium

✍ Doron Sonsino 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 410 KB

Lower bounds in pattern recognition and

Lower bounds in pattern recognition and learning

✍ Luc Devroye; Gábor Lugosi 📂 Article 📅 1995 🏛 Elsevier Science 🌐 English ⚖ 477 KB