Leonard E. Trigg

Joint Variant and De Novo Mutation Identification on Pedigrees from High-Throughput Sequencing Data

John G. Cleary, Francisco M. de la Vega, Alan Jackson, 2014, bioRxiv.

An entropy gain measure of numeric prediction performance

Leonard E. Trigg, 1998 .

A diagnostic tool for tree based supervised classification learning algorithms

Geoffrey Holmes, Leonard E. Trigg, Geoffrey Holmes, 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

Experiences with a weighted decision tree learner

Geoffrey Holmes, John G. Cleary, Mark A. Hall, 2001 .

Objective measurement of mushroom quality relative to industry inspectors

Geoffrey Holmes, Leonard E. Trigg, A. F. Bollen, 2006 .

K*: An Instance-based Learner Using and Entropic Distance Measure

John G. Cleary, Leonard E. Trigg, J. Cleary, 1995, ICML.

Experiences with OB1, An Optimal Bayes Decision Tree Learner

John G. Cleary, Leonard E. Trigg, 1998 .

Naive Bayes for Regression (Technical Note)

Ian H. Witten, Geoff Holmes, Eibe Frank, 2000, Machine-mediated learning.

Data mining in bioinformatics using Weka

Ian H. Witten, Geoff Holmes, Eibe Frank, 2004, Bioinform..

Naive Bayes for regression

Leonard E. Trigg, I. Witten, Eibe Frank, 1998 .

Weka-A Machine Learning Workbench for Data Mining

Ian H. Witten, Bernhard Pfahringer, Richard Kirkby, 2005, Data Mining and Knowledge Discovery Handbook.

Technical Note: Naive Bayes for Regression

Leonard E. Trigg, I. Witten, Eibe Frank, 2000, Machine Learning.

Weka: Practical machine learning tools and techniques with Java implementations

Ian H. Witten, Sally Jo Cunningham, Geoffrey Holmes, 1999 .