[کتاب][B] Machine learning, neural and statistical classification

D Michie, DJ Spiegelhalter, CC Taylor, J Campbell - 1995‏ - dl.acm.org
Machine learning, neural and statistical classification | Guide books skip to main content ACM
Digital Library home ACM Association for Computing Machinery corporate logo Google, Inc …

Inductive logic programming at 30: a new introduction

A Cropper, S Dumančić - Journal of Artificial Intelligence Research, 2022‏ - jair.org
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Induction of logic programs: FOIL and related systems

JR Quinlan, RM Cameron-Jones - New Generation Computing, 1995‏ - Springer
FOIL is a first-order learning system that uses information in a collection of relations to
construct theories expressed in a dialect of Prolog. This paper provides an overview of the …

Transductive confidence machines for pattern recognition

K Proedrou, I Nouretdinov, V Vovk… - Machine Learning: ECML …, 2002‏ - Springer
We propose a new algorithm for pattern recognition that outputs some measures of
“reliability” for every prediction made, in contrast to the current algorithms that output “bare” …

[PDF][PDF] Machine learning in games: A survey

J Fürnkranz - Machines that learn to play games, 2001‏ - fmfi-uk.hq.sk
This paper provides a survey of previously published work on machine learning in game
playing. The material is organized around a variety of problems that typically arise in game …

Inductive general game playing

A Cropper, R Evans, M Law - Machine Learning, 2020‏ - Springer
General game playing (GGP) is a framework for evaluating an agent's general intelligence
across a wide range of tasks. In the GGP competition, an agent is given the rules of a game …

GP-endchess: Using genetic programming to evolve chess endgame players

A Hauptman, M Sipper - European Conference on Genetic Programming, 2005‏ - Springer
We apply genetic programming to the evolution of strategies for playing chess endgames.
Our evolved programs are able to draw or win against an expert human-based strategy, and …

MICAR: nonlinear association rule mining based on maximal information coefficient

M Liu, Z Yang, Y Guo, J Jiang, K Yang - Knowledge and Information …, 2022‏ - Springer
Association rule mining (ARM) is an important research issue in data mining and knowledge
discovery. Existing ARM methods cannot discover nonlinear association rules, despite …

Machine learning in computer chess: The next generation

J Fürnkranz - ICGA Journal, 1996‏ - journals.sagepub.com
Ten years ago the ICCA Journal published an overview of machine-learning approaches to
computer chess (Skiena, 1986). The author's results were rather pessimistic. In particular he …

[PDF][PDF] A Randomized ANOVA Procedure for Comparing Performance Curves.

JH Piater, PR Cohen, X Zhang, M Atighetchi - ICML, 1998‏ - sci2s.ugr.es
Three factors are related in analyses of performance curves such as learning curves: the
amount of training, the learning algorithm, and performance. Often we want to know whether …