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[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 …
Digital Library home ACM Association for Computing Machinery corporate logo Google, Inc …
Inductive logic programming at 30: a new introduction
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 …
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …
Induction of logic programs: FOIL and related systems
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 …
construct theories expressed in a dialect of Prolog. This paper provides an overview of the …
Transductive confidence machines for pattern recognition
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” …
“reliability” for every prediction made, in contrast to the current algorithms that output “bare” …
[PDF][PDF] Machine learning in games: A survey
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 …
playing. The material is organized around a variety of problems that typically arise in game …
Inductive general game playing
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 …
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
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 …
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
Association rule mining (ARM) is an important research issue in data mining and knowledge
discovery. Existing ARM methods cannot discover nonlinear association rules, despite …
discovery. Existing ARM methods cannot discover nonlinear association rules, despite …
Machine learning in computer chess: The next generation
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 …
computer chess (Skiena, 1986). The author's results were rather pessimistic. In particular he …
[PDF][PDF] A Randomized ANOVA Procedure for Comparing Performance Curves.
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 …
amount of training, the learning algorithm, and performance. Often we want to know whether …