Absolutely no free lunches!
From MaRDI portal
Publication:2207503
DOI10.1016/j.tcs.2020.09.013zbMath1454.68117arXiv2005.04791OpenAlexW3022956916MaRDI QIDQ2207503
Publication date: 22 October 2020
Published in: Theoretical Computer Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.04791
Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
Related Items (2)
IS CAUSAL REASONING HARDER THAN PROBABILISTIC REASONING? ⋮ The no-free-lunch theorems of supervised learning
Cites Work
- 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
- Identification of probabilities
- Comparison of identification criteria for machine inductive inference
- Program synthesis in the presence of infinite number of inaccuracies
- Learning recursive functions: A survey
- Effectively closed sets of measures and randomness
- On the relative sizes of learnable sets
- Research in the theory of inductive inference by GDR mathematicians - A survey
- The speed of rational learning
- Classical recursion theory. Vol. II
- Weak and strong merging of opinions
- Baire category in spaces of measures
- Simple explanation of the no-free-lunch theorem and its implications
- Algorithmic identification of probabilities is hard
- Equivalences between learning of data and probability distributions, and their applications
- Putnam's diagonal argument and the impossibility of a universal learning machine
- On universal prediction and Bayesian confirmation
- Measurable sets of measures
- On the Computability of Solomonoff Induction and Knowledge-Seeking
- Set Theory
- Generic computability, Turing degrees, and asymptotic density
- A Computable Universe
- Statistical Learning Theory: Models, Concepts, and Results
- Non-Bayesian Testing of a Stochastic Prediction
- Automorphisms of the Rational World
- Probabilistic inductive inference
- Merging of Opinions with Increasing Information
- THE BANACH-MAZUR GAME
- Toward a mathematical theory of inductive inference
- Complexity-based induction systems: Comparisons and convergence theorems
- Merging with a set of probability measures: A characterization
- Inductive inference of approximations
- Remarques sur les fonctions complètement additives d'ensemble et sur les ensembles jouissant de la propriété de Baire
- Killing Residual Measures
- Understanding Machine Learning
- On the Asymptotic Behavior of Bayes' Estimates in the Discrete Case
- C(X)* and Kolmogorov's Consistency Theorem for Cantor Spaces
- A formal theory of inductive inference. Part I
- Language identification in the limit
- Remarks on measure and category
- An introduction to Kolmogorov complexity and its applications
This page was built for publication: Absolutely no free lunches!
