## J R Quinlan## School of Computer Science and Engineering UNSW, 2052, Sydney, Australia | University of Sydney, 2006, Sydney, N.S.W., Australia | University of Sydney, Sydney, Australia | ... | |

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## J R Quinlan:Expert Impact

Concepts for which**J R Quinlan**has direct influence:**Small disjuncts**,**Continuous attributes**,**Plausible reasoning**,**Logical definitions**,**Decision tree classifiers**,**Training examples**,**Cautious approach**,**Learning systems**.

## J R Quinlan:KOL impact

Concepts related to the work of other authors for whichfor which J R Quinlan has influence:**Decision tree**,**Machine learning**,**Data mining**,**Feature selection**,**Random forest**,**Neural networks**,**Artificial intelligence**.

## KOL Resume for J R Quinlan

Year | |
---|---|

1999 | School of Computer Science and Engineering UNSW, 2052, Sydney, Australia |

1996 | University of Sydney, 2006, Sydney, N.S.W., Australia |

1991 | Basser Department of Computer Science, University of Sydney, 2006, Sydney, Australia |

1990 | Basser Department of Computer Science, University of Sydney, 2006, Sydney, NSW, Australia |

1986 | Centre for Advanced Computing Sciences, New South Wales Institute of Technology, 2007, Sydney, Australia |

1985 | School of Computing Sciences, New South Wales Institute of Technology, 2007, Sydney, Australia |

1983 | School of Computing Sciences, NSWIT, PO Box 123, Broadway, New South Wales, Australia |

1968 | University of Washington, Computer Science Group, Seattle, Washington |

Concept | World rank |
---|---|

functional relations evidence | #1 |

plausible reasoning systems | #1 |

firstorder learning | #1 |

relationsfoil ideas | #1 |

horn clauses data | #1 |

relationsfoil | #1 |

fortran deductive | #1 |

rule validity measure | #1 |

firstorder formalism new | #1 |

early experimental ffoil | #1 |

consistent subsets evidence | #1 |

evidence firstorder induction | #1 |

comparative implementations | #1 |

firstorder definitions | #1 |

small task analysis | #1 |

evidence small task | #1 |

deductive problem | #1 |

attributevalue learning systems | #1 |

classifierlearning systems | #1 |

ffoil | #1 |

validity measure deduction | #1 |

relations paper describesfoil | #1 |

logical definitions relations | #1 |

boosting firstorder learning | #1 |

data relationsfoil | #1 |

conjunction comparative | #1 |

definitions functional relations | #1 |

ffoil firstorder | #1 |

paper describesfoil | #1 |

validity measure fact | #1 |

smaller decision trees | #1 |

fact rule | #2 |

disjunct | #2 |

paper early experimental | #2 |

boosting order | #2 |

predictive accuracy paper | #2 |

domains continuous attributes | #3 |

datamining applications | #3 |

global discretization | #3 |

firstorder induction | #3 |

deduction approach | #4 |

kohavi | #4 |

approach plausible | #4 |

uncertain inference | #4 |

decision tree classifiers | #5 |

learning logical | #5 |

deficiencies case | #5 |

small disjuncts | #5 |

implementations systems | #6 |

higher predictive accuracies | #6 |

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**Prominent publications by J R Quinlan**

The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing **decision trees** that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic ...

Known for | Decision Trees | Inductive Inference | Practical Applications | Basic AlgorithmPaper Approach |

This paper describesfoil, a system that learns Horn clauses from data expressed as relations.foil is based on ideas that have **proved effective** in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.

Known for | Logical Definitions | Horn Clauses | Machine Learning Literature | Learning SystemsRelations Paper |

A formalism is defined in which solutions to theorem-proving and similar problems take the form of a sequence of transformations of states into other states. The data used to solve a problem then consists of a set of rewriting rules which defines the allowable transformations. A method for selecting “useful” transformations, i.e. those which will most probably lead to a solution, is developed. Two problem-solving processes based on the above are defined; one, called the **FORTRAN Deductive** ...

Known for | Fortran Deductive | Rewriting Rules | Problem Solving | Theorem Proving“ ” |

The usual approach to plausible reasoning is to associate a **validity measure** with each fact or rule, and to compute from these a validity measure for any deduction that is made. This approach is shown to be inappropriate for some classes of problems, particularly those in which the evidence is not internally consistent. Three current plausible reasoning architectures are summarised and each applied to the same small task. An analysis of the performance of these systems reveals ...

Several empirical studies have confirmed that boosting classifier-learning systems can lead to **substantial improvements** in predictive accuracy. This paper reports **early experimental** results from applying boosting to ffoil, a first-order system that constructs definitions of functional relations. Although the evidence is less convincing than that for propositional-level learning systems, it suggests that boosting will also prove beneficial for first-order induction.

Known for | Order Learning | Predictive AccuracyEmpirical Studies |

This talk will revisit some important elements of ML lore, focusing on the design of classifier-learning systems. Within ML, the key desiderata for such systems have been predictive accuracy and interpretability. Although Provost, Fawcett and Kohavi (1998) have shown that accuracy alone is a poor metric for comparing learning systems, it is still important in most real-world applications. The quest for intelligibility, stressed from earliest days by Michie, Michalski and others, is now ...

Known for | Predictive AccuracyData Mining |

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