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About 2,491 papers for “Algorithmic learning theory”

Algorithmic Learning Theory

A. Sharma, 渡辺 治, O Watanabe · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2008

Cited by 0

Algorithmic Learning Theory

Michael M. Richter, Thomas Zeugmann, Rolf Wiehagen et al. · Lecture notes in computer science · 1998

Cited by 20

Algorithmic Learning Theory

Carbonell, Jaime G, Zeugmann, Thomas, Turán, György et al. · Lecture notes in computer science · 2008

Cited by 25

An Algorithmic Theory for Conscious Learning

Juyang Weng · 2022

The new conscious learning mode here is end-to-end (3D-to-2D-to-3D) and free from annotations of 2D images and 2D motor images, such as a bounding box for a patch to be attended to. The algorithm directly takes that of the Developmental Networks that has been previously published extensively with rich experimental results. This paper fills the huge gap between 3D world, to 2D sensory images and 2D motor images, back to 3D world so the conscious learning is end-to-end without a need for motor-impositions. This new conscious learning methodology is a major departure from traditional AI—handcrafting symbolic labels that tend to be brittle (e.g., for driverless cars) and then “spoon-feeding” pre-collected “big data”. The analysis here establishes that autonomous imitations as presented are a general mechanism in learning universal Turing machines. Autonomous imitations drastically reduce the teaching complexity compared to pre-collected “big data”, especially because no annotations of training data are needed. This learning mode is technically supported by a new kind of neural networks called Developmental Network-2 (DN-2) as an algorithmic basis, due to its incremental, non-iterative, on-the-fly learning mode along with the optimality (in the sense of maximum likelihood) in learning emergent super Turing machines from the open-ended real physical world. This work is directly related to electronics engineering because it requires large-scale on-the-fly brainoid chips in conscious learning robots.

Cited by 13

Algorithmic game theory and machine learning

David Sychrovský · Digital Repository (National Repository of Grey Literature) · 2025

In this thesis we study two frameworks which combine game theory and multi-agent learning. The first describes an extension of the free market, designed to promote fair allocation of scarce resources. We prove theoretically the system achieves the goal in restricted settings, and demonstrate its generality empirically. In the second part, we describe a meta-learning framework over regret minimization algorithms. We use our approach to accelerate convergence to equilibrium in two-player zero-sum games, and to approximate Nash equilibra in general-sum games. In both cases, we outperform state- of-the-art algorithms on the domains they were designed to excel at.

Cited by 0

Algorithmic Learning Theory with Elementary Formal Systems

Setsuo Arikawa, 節夫 有川, Satoru Miyano et al. · Kyushu University Institutional Repository (QIR) (Kyushu University) · 1992

The elementary formal system (EFS, for short) is a kind of logic program which directly manipulates character strings. This paper outlines in brief the authors' studies on algorithmic learning theory developed in the framework of EFS's. We define two important classes of EFS's and a new hierarchy of various language classes. Then we discuss EFS's as logic programs. We show that EFS's form a good framework for inductive inference of languages by presenting model inference system for EFS's in Shapiro's sense. Using the framework we also show that inductive inference from positive data and PAC-learning are both much more powerful than they have been believed. We illustrate an application of our theoretical results to Molecular Biology.

Cited by 11Open access

Learning in Natural Language: Theory and Algorithmic Approaches

Dan Roth · 2000

This article summarizes work on developing a learning theory account for the major learning and statistics based approaches used in natural language processing. It shows that these approaches can all be explained using a single distribution free inductive principle related to the pac model of learning. Furthermore, they all make predictions using the same simple knowledge representation -- a linear representation over a common feature space. This is significant both to explaining the generalization and robustness properties of these methods and to understanding how these methods might be extended to learn from more structured, knowledge intensive examples, as part of a learning centered approach to higher level natural language inferences.

Cited by 0