Geoffrey I. Webb

According to our database1, Geoffrey I. Webb authored at least 230 papers between 1988 and 2021.

Collaborative distances:

Awards

IEEE Fellow

IEEE Fellow 2015, "For contributions to machine learning, data mining and knowledge discovery".

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2021
Tight lower bounds for dynamic time warping.
Pattern Recognit., 2021

Time series extrinsic regression.
Data Min. Knowl. Discov., 2021

Elastic Similarity Measures for Multivariate Time Series Classification.
CoRR, 2021

Early Abandoning and Pruning for Elastic Distances.
CoRR, 2021

MultiRocket: Effective summary statistics for convolutional outputs in time series classification.
CoRR, 2021

Better Short than Greedy: Interpretable Models through Optimal Rule Boosting.
CoRR, 2021

2020
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams.
IEEE Trans. Fuzzy Syst., 2020

A novel selective naïve Bayes algorithm.
Knowl. Based Syst., 2020

PCA-based drift and shift quantification framework for multidimensional data.
Knowl. Inf. Syst., 2020

PROSPECT: A web server for predicting protein histidine phosphorylation sites.
J. Bioinform. Comput. Biol., 2020

Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information.
Genom. Proteom. Bioinform., 2020

FastEE: Fast Ensembles of Elastic Distances for time series classification.
Data Min. Knowl. Discov., 2020

TS-CHIEF: a scalable and accurate forest algorithm for time series classification.
Data Min. Knowl. Discov., 2020

InceptionTime: Finding AlexNet for time series classification.
Data Min. Knowl. Discov., 2020

ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels.
Data Min. Knowl. Discov., 2020

Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372).
Dagstuhl Reports, 2020

Ensembles of Localised Models for Time Series Forecasting.
CoRR, 2020

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification.
CoRR, 2020

Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning.
CoRR, 2020

An Eager Splitting Strategy for Online Decision Trees.
CoRR, 2020

Emergent and Unspecified Behaviors in Streaming Decision Trees.
CoRR, 2020

A Strong Baseline for Weekly Time Series Forecasting.
CoRR, 2020

Early Abandoning PrunedDTW and its application to similarity search.
CoRR, 2020

Time Series Regression.
CoRR, 2020

Monash University, UEA, UCR Time Series Regression Archive.
CoRR, 2020

A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping.
CoRR, 2020

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites.
Bioinform., 2020

PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact.
Briefings Bioinform., 2020

Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.
Briefings Bioinform., 2020

iLearn : an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.
Briefings Bioinform., 2020

On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers.
IEEE Access, 2020

Time Series Classification at Scale.
Proceedings of the Conference "Lernen, 2020

Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series.
Remote. Sens., 2019

Survey of distance measures for quantifying concept drift and shift in numeric data.
Knowl. Inf. Syst., 2019

Adaptive online extreme learning machine by regulating forgetting factor by concept drift map.
Neurocomputing, 2019

Proximity Forest: an effective and scalable distance-based classifier for time series.
Data Min. Knowl. Discov., 2019

A tutorial on statistically sound pattern discovery.
Data Min. Knowl. Discov., 2019

Time series classification for varying length series.
CoRR, 2019

SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models.
BMC Bioinform., 2019

Positive-unlabelled learning of glycosylation sites in the human proteome.
BMC Bioinform., 2019

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.
Briefings Bioinform., 2019

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.
Briefings Bioinform., 2019

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.
Briefings Bioinform., 2019

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.
Briefings Bioinform., 2019

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.
Briefings Bioinform., 2019

Elastic bands across the path: A new framework and method to lower bound DTW.
Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Using Sentinel-2 Image Time Series to map the State of Victoria, Australia.
Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images, 2019

Exploring Data Quantity Requirements for Domain Adaptation in the Classification of Satellite Image Time Series.
Proceedings of the 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images, 2019

Deep Learning for the Classification of Sentinel-2 Image Time Series.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

2018
Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes.
Mach. Learn., 2018

Mining significant crisp-fuzzy spatial association rules.
Int. J. Geogr. Inf. Sci., 2018

Analyzing concept drift and shift from sample data.
Data Min. Knowl. Discov., 2018

Elastic bands across the path: A new framework and methods to lower bound DTW.
CoRR, 2018

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams.
CoRR, 2018

Instance-Dependent PU Learning by Bayesian Optimal Relabeling.
CoRR, 2018

On the Inter-relationships among Drift rate, Forgetting rate, Bias/variance profile and Error.
CoRR, 2018

PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.
Bioinform., 2018

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.
Bioinform., 2018

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.
Briefings Bioinform., 2018

Efficient and Effective Accelerated Hierarchical Higher-Order Logistic Regression for Large Data Quantities.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Efficient search of the best warping window for Dynamic Time Warping.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Extremely Fast Decision Tree.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017
Tree Augmented Naive Bayes.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Semi-naive Bayesian Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Averaged One-Dependence Estimators.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Prior Probability.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Posterior Probability.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Overfitting.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Occam's Razor.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Naïve Bayes.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

MultiBoosting.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Model Evaluation.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Lazy Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Evaluation of Learning Algorithms.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Bayes' Rule.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Algorithm Evaluation.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Supervised Descriptive Rule Induction.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Generative and Discriminative Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Data Preparation.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Sample-Based Attribute Selective A<i>n</i> DE for Large Data.
IEEE Trans. Knowl. Data Eng., 2017

Efficient parameter learning of Bayesian network classifiers.
Mach. Learn., 2017

Selective AnDE for large data learning: a low-bias memory constrained approach.
Knowl. Inf. Syst., 2017

SimUSF: an efficient and effective similarity measure that is invariant to violations of the interval scale assumption.
Data Min. Knowl. Discov., 2017

Understanding Concept Drift.
CoRR, 2017

POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.
Bioinform., 2017

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.
Briefings Bioinform., 2017

A Fast Trust-Region Newton Method for Softmax Logistic Regression.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Indexing and classifying gigabytes of time series under time warping.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Generating Synthetic Time Series to Augment Sparse Datasets.
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

2016
ALR<sup>n</sup>: accelerated higher-order logistic regression.
Mach. Learn., 2016

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm.
Knowl. Inf. Syst., 2016

Scalable Learning of Bayesian Network Classifiers.
J. Mach. Learn. Res., 2016

Mining significant association rules from uncertain data.
Data Min. Knowl. Discov., 2016

Characterizing concept drift.
Data Min. Knowl. Discov., 2016

Skopus: Mining top-k sequential patterns under leverage.
Data Min. Knowl. Discov., 2016

Preconditioning an Artificial Neural Network Using Naive Bayes.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Scalable Learning of Graphical Models.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015
Introduction: special issue of selected papers of ACML 2013.
Mach. Learn., 2015

Deep Broad Learning - Big Models for Big Data.
CoRR, 2015

Exact discovery of the most interesting sequential patterns.
CoRR, 2015

GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome.
Bioinform., 2015

Scaling log-linear analysis to datasets with thousands of variables.
Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

2014
A Data Scientist's Guide to Start-Ups.
Big Data, 2014

Preface to the 1st ECML/PKDD workshop on Statistically Sound Data Mining.
Proceedings of the 1st ECML/PKDD Workshop on Statistically Sound Data Mining, 2014

Highly Scalable Attribute Selection for Averaged One-Dependence Estimators.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Does social good justify risking personal privacy?
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Statistically sound pattern discovery.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Naive-Bayes Inspired Effective Pre-Conditioner for Speeding-Up Logistic Regression.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Contrary to Popular Belief Incremental Discretization can be Sound, Computationally Efficient and Extremely Useful for Streaming Data.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

A Statistically Efficient and Scalable Method for Log-Linear Analysis of High-Dimensional Data.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013
Efficient Discovery of the Most Interesting Associations.
ACM Trans. Knowl. Discov. Data, 2013

Alleviating naive Bayes attribute independence assumption by attribute weighting.
J. Mach. Learn. Res., 2013

Fast and Effective Single Pass Bayesian Learning.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

Panel: a data scientist's guide to making money from start-ups.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Scaling Log-Linear Analysis to High-Dimensional Data.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

2012
Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning.
Mach. Learn., 2012

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification.
Mach. Learn., 2012

Techniques for Efficient Learning without Search.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2012

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers.
Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012

Discovering Associations in High-Dimensional Data.
Proceedings of the Twenty-Third Australasian Database Conference, 2012

2011
Filtered-top-<i>k</i> association discovery.
Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 2011

Feature-subspace aggregating: ensembles for stable and unstable learners.
Mach. Learn., 2011

Bioinformatic Approaches for Predicting substrates of Proteases.
J. Bioinform. Comput. Biol., 2011

2010
Tree Augmented Naive Bayes.
Proceedings of the Encyclopedia of Machine Learning, 2010

Semi-Naive Bayesian Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Averaged One-Dependence Estimators.
Proceedings of the Encyclopedia of Machine Learning, 2010

Prior Probability.
Proceedings of the Encyclopedia of Machine Learning, 2010

Posterior Probability.
Proceedings of the Encyclopedia of Machine Learning, 2010

Overfitting.
Proceedings of the Encyclopedia of Machine Learning, 2010

Occam's Razor.
Proceedings of the Encyclopedia of Machine Learning, 2010

Naïve Bayes.
Proceedings of the Encyclopedia of Machine Learning, 2010

MultiBoosting.
Proceedings of the Encyclopedia of Machine Learning, 2010

Model Evaluation.
Proceedings of the Encyclopedia of Machine Learning, 2010

Lazy Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Data Preparation.
Proceedings of the Encyclopedia of Machine Learning, 2010

Bayes Rule.
Proceedings of the Encyclopedia of Machine Learning, 2010

Algorithm Evaluation.
Proceedings of the Encyclopedia of Machine Learning, 2010

Supervised Descriptive Rule Induction.
Proceedings of the Encyclopedia of Machine Learning, 2010

Generative and Discriminative Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Discretization Methods.
Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

Self-sufficient itemsets: An approach to screening potentially interesting associations between items.
ACM Trans. Knowl. Discov. Data, 2010

Cascleave: towards more accurate prediction of caspase substrate cleavage sites.
Bioinform., 2010

EGM: encapsulated gene-by-gene matching to identify gene orthologs and homologous segments in genomes.
Bioinform., 2010

2009
Discretization for naive-Bayes learning: managing discretization bias and variance.
Mach. Learn., 2009

Anytime classification for a pool of instances.
Mach. Learn., 2009

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining.
J. Mach. Learn. Res., 2009

A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2009

FaSS: Ensembles for Stable Learners.
Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

2008
Layered critical values: a powerful direct-adjustment approach to discovering significant patterns.
Mach. Learn., 2008

Multi-Strategy Ensemble Learning, Ensembles of Bayesian Classifiers, and the Problem of False Discoveries.
Proceedings of the Data Mining and Analytics 2008, 2008

2007
To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators.
IEEE Trans. Knowl. Data Eng., 2007

Classifying under computational resource constraints: anytime classification using probabilistic estimators.
Mach. Learn., 2007

Discovering Significant Patterns.
Mach. Learn., 2007

Editorial.
Data Min. Knowl. Discov., 2007

Finding the Real Patterns.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2007

Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators.
Proceedings of the Machine Learning: ECML 2007, 2007

2006
Discovering significant rules.
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Efficient lazy elimination for averaged one-dependence estimators.
Proceedings of the Machine Learning, 2006

To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles.
Proceedings of the Machine Learning: ECML 2006, 2006

Generality Is Predictive of Prediction Accuracy.
Proceedings of the Data Mining - Theory, Methodology, Techniques, and Applications, 2006

Efficiently Identifying Exploratory Rules' Significance.
Proceedings of the Data Mining - Theory, Methodology, Techniques, and Applications, 2006

Anytime learning and classification for online applications.
Proceedings of the Advances in Intelligent IT, 2006

Incremental Discretization for Naïve-Bayes Classifier.
Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006

2005
On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.
Mach. Learn., 2005

Not So Naive Bayes: Aggregating One-Dependence Estimators.
Mach. Learn., 2005

K-Optimal Rule Discovery.
Data Min. Knowl. Discov., 2005

Discarding Insignificant Rules during Impact Rule Discovery in Large, Dense Databases.
Proceedings of the 2005 SIAM International Conference on Data Mining, 2005

Pruning Derivative Partial Rules During Impact Rule Discovery.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2005

Ensemble Selection for SuperParent-One-Dependence Estimators.
Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005

K-Optimal Pattern Discovery: An Efficient and Effective Approach to Exploratory Data Mining.
Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005

Discretization Methods.
Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

2004
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques.
IEEE Trans. Knowl. Data Eng., 2004

Guest Editors' Introduction.
Int. J. Softw. Eng. Knowl. Eng., 2004

Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2004

Mining Negative Rules Using GRD.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2004

2003
Identifying Approximate Itemsets of Interest in Large Databases.
Appl. Intell., 2003

Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2003

A New Restricted Bayesian Network Classifier.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2003

On detecting differences between groups.
Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

On Why Discretization Works for Naive-Bayes Classifiers.
Proceedings of the AI 2003: Advances in Artificial Intelligence, 2003

Adjusting Dependence Relations for Semi-Lazy TAN Classifiers.
Proceedings of the AI 2003: Advances in Artificial Intelligence, 2003

A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images.
Proceedings of the AI 2003: Advances in Artificial Intelligence, 2003

2002
The Need for Low Bias Algorithms in Classification Learning from Large Data Sets.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 2002

Non-Disjoint Discretization for Naive-Bayes Classifiers.
Proceedings of the Machine Learning, 2002

Comparison of Lazy Bayesian Rule and Tree-Augmented Bayesian Learning.
Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002

Experimentation and Self Learning in Continuous Database Marketing.
Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002

Averaged One-Dependence Estimators: Preliminary Results.
Proceedings of the 15th Australian Joint Conference on Artificial Intelligence 2002, 2002

A Heuristic Lazy Bayesian Rule Algorithm.
Proceedings of the 15th Australian Joint Conference on Artificial Intelligence 2002, 2002

Solving Regression Problems Using Competitive Ensemble Models.
Proceedings of the AI 2002: Advances in Artificial Intelligence, 2002

2001
Machine Learning for User Modeling.
User Model. User Adapt. Interact., 2001

Discovering associations with numeric variables.
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001

Proportional k-Interval Discretization for Naive-Bayes Classifiers.
Proceedings of the Machine Learning: EMCL 2001, 2001

Further Pruning for Efficient Association Rule Discovery.
Proceedings of the AI 2001: Advances in Artificial Intelligence, 2001

Candidate Elimination Criteria for Lazy Bayesian Rules.
Proceedings of the AI 2001: Advances in Artificial Intelligence, 2001

2000
Lazy Learning of Bayesian Rules.
Mach. Learn., 2000

MultiBoosting: A Technique for Combining Boosting and Wagging.
Mach. Learn., 2000

Efficient search for association rules.
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000

1999
An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition.
Mach. Learn., 1999

Stochastic Attribute Selection Committees with Aultiple Boosting: Learning More Accurate and More Stable Classifer Committees.
Proceedings of the Methodologies for Knowledge Discovery and Data Mining, 1999

Convex Hulls in Concept Induction.
Proceedings of the Methodologies for Knowledge Discovery and Data Mining, 1999

Decision Tree Grafting From the All Tests But One Partition.
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999

Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees.
Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999

1998
Preface to UMUAI Special Issue on Machine Learning for User Modeling.
User Model. User Adapt. Interact., 1998

Using Decision Trees for Agent Modeling: Improving Prediction Performance.
User Model. User Adapt. Interact., 1998

Evaluation of Data Aging: A Technique for Discounting Old Data During Student Modeling.
Proceedings of the Intelligent Tutoring Systems, 4th International Conference, 1998

Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning.
Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence, 1998

Classification Learning Using All Rules.
Proceedings of the Machine Learning: ECML-98, 1998

Stochastic Attribute Selection Committees.
Proceedings of the Advanced Topics in Artificial Intelligence, 1998

Adjusted Probability Naive Bayesian Induction.
Proceedings of the Advanced Topics in Artificial Intelligence, 1998

The Problem of Missing Values in Decision Tree Grafting.
Proceedings of the Advanced Topics in Artificial Intelligence, 1998

1997
Decision Tree Grafting.
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

Using Decision Trees for Agent Modelling: A Study on Resolving Confliction Predictions.
Proceedings of the Advanced Topics in Artificial Intelligence, 1997

1996
Integrating machine learning with knowledge acquisition through direct interaction with domain experts.
Knowl. Based Syst., 1996

Further Experimental Evidence against the Utility of Occam's Razor.
J. Artif. Intell. Res., 1996

Cost-Sensitive Specialization.
Proceedings of the PRICAI'96: Topics in Artificial Intelligence, 1996

1995
Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents' Competencies.
User Model. User Adapt. Interact., 1995

OPUS: An Efficient Admissible Algorithm for Unordered Search.
J. Artif. Intell. Res., 1995

Polygonal Inductive Generalisation System.
Proceedings of the Eighth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1995

Transparency Debugging with Explanations for Novice Programmers.
Proceedings of the Second International Workshop on Automated Debugging, 1995

1992
Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm.
Artif. Intell. Medicine, 1992

Evaluation of Feature Based Modelling in Subtraction.
Proceedings of the Intelligent Tutoring Systems, Second International Conference, 1992

1990
Improving the efficiency of rule-based expert systems by rule activation.
J. Exp. Theor. Artif. Intell., 1990

1988
A Knowledge-Based Approach to Computer-Aided Learning.
Int. J. Man Mach. Stud., 1988

Techniques for Efficient Empirical Induction.
Proceedings of the AI '88: 2nd Australian Joint Artificial Intelligence Conference, 1988


  Loading...