Playing monotone games to understand learning behaviors

DOI10.1016/j.tcs.2010.02.011zbMath1208.68131DBLPjournals/tcs/ApolloniBGMZ10OpenAlexW1966795445WikidataQ62840551 ScholiaQ62840551MaRDI QIDQ974103

Dario Malchiodi, Sabrina Gaito, Italo Zoppis, Simone Bassis, Bruno Apolloni

Publication date: 27 May 2010

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2010.02.011

zbMATH Keywords

computational learningmonotone gamesalgorithmic inferenceovertraining controlsubsymbolic learningtraining stopping rule

Mathematics Subject Classification ID

Computational learning theory (68Q32) Game theory (91A99)

Related Items (1)

Algorithmic inference: From information granules to subtending functions

Cites Work

• Monotone games with positive spillovers
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Calibrated learning and correlated equilibrium
• PAC learning of concept classes through the boundaries of their items
• Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term
• Non-cooperative games
• Rational Learning Leads to Nash Equilibrium
• Learnability and the Vapnik-Chervonenkis dimension
• A theory of the learnable
• Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems
• Approximate Algorithms for the 0/1 Knapsack Problem
• On Information in Statistics
• Non-Parametric Estimation II. Statistically Equivalent Blocks and Tolerance Regions--The Continuous Case
• Gaining degrees of freedom in subsymbolic learning
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Playing monotone games to understand learning behaviors