Geoff Holmes

发表

Biosensing by Learning: Cancer Detection as Iterative optimization

Yifan Chen, Neda Sharifi, Geoff Holmes, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Natural language and computational linguistics

Geoff Holmes, Colin Beardon, David Lumsden, 1991 .

Teaching computer systems to majors: a MIPS based solution

Geoff Holmes, Murray Pearson, Tony McGregor, 1998, WCAE '98.

Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data

Geoff Holmes, Jesse Read, Albert Bifet, 2012, IDA.

Clustering Large Datasets Using Cobweb and K-Means in Tandem

Geoff Holmes, Mi Li, Bernhard Pfahringer, 2004, Australian Conference on Artificial Intelligence.

MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, Discovery Science.

Wrapping Boosters against Noise

Geoff Holmes, Bernhard Pfahringer, Gabi Schmidberger, 2001, Australian Joint Conference on Artificial Intelligence.

Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016)

Kee-Eung Kim, Zhi-Hua Zhou, Masashi Sugiyama, 2017, Machine Learning.

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

Using data mining to support the construction and maintenance of expert systems

Sally Jo Cunningham, Geoff Holmes, G. Holmes, 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

Correction to: Adaptive random forests for evolving data stream classification

Talel Abdessalem, Geoff Holmes, Jean Paul Barddal, 2019, Machine Learning.

Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search

Geoff Holmes, Xing Wu, Bernhard Pfahringer, 2008, Australasian Conference on Artificial Intelligence.

Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, ACML.

Handling Numeric Attributes in Hoeffding Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2008, PAKDD.

The WEKA data mining software: an update

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2009, SKDD.

WEKA - Experiences with a Java Open-Source Project

Ian H. Witten, Geoff Holmes, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

12 The Graphical User Interface

Albert Bifet, Geoff Holmes, Ricard Gavaldà, 2018 .

Multinomial Naive Bayes for Text Categorization Revisited

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2004, Australian Conference on Artificial Intelligence.

Ensembles of Restricted Hoeffding Trees

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2012, TIST.

CD-MOA: Change Detection Framework for Massive Online Analysis

Geoff Holmes, Jesse Read, Albert Bifet, 2013, IDA.

Benchmarking Attribute Selection Techniques for Discrete Class Data Mining

Geoff Holmes, Mark A. Hall, M. Hall, 2003, IEEE Trans. Knowl. Data Eng..

Adaptive random forests for evolving data stream classification

Talel Abdessalem, Geoff Holmes, Jean Paul Barddal, 2017, Machine Learning.

Scalable Multi-label Classification

Geoff Holmes, Bernhard Pfahringer, B. Pfahringer, 2010 .

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

Geoff Holmes, Jesse Read, Albert Bifet, 2013, ECML/PKDD.

Multi-label Classification Using Ensembles of Pruned Sets

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Mining frequent closed graphs on evolving data streams

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2011, KDD.

Correcting English text using PPM models

Geoff Holmes, John G. Cleary, William John Teahan, 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

Active Learning With Drifting Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Probability Calibration Trees pdf

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2017, ACML.

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2015, 2015 IEEE International Conference on Data Mining.

Algorithm Selection on Data Streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2014, Discovery Science.

Streaming Multi-label Classification

Geoff Holmes, Jesse Read, Albert Bifet, 2011, WAPA.

MOA Concept Drift Active Learning Strategies for Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, WAPA.

Organizing the World's Machine Learning Information

Geoff Holmes, Hendrik Blockeel, Joaquin Vanschoren, 2008, ISoLA.

Racing Committees for Large Datasets

Geoff Holmes, Eibe Frank, Mark A. Hall, 2002, Discovery Science.

Learning from the Past with Experiment Databases

Geoff Holmes, Joaquin Vanschoren, Bernhard Pfahringer, 2008, PRICAI.

Interactive machine learning: letting users build classifiers

Ian H. Witten, Geoff Holmes, Eibe Frank, 2002, Int. J. Hum. Comput. Stud..

MOA: Massive Online Analysis

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, J. Mach. Learn. Res..

5 Dealing with Change

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2018 .

A Modified Quickprop Algorithm

Geoff Holmes, Alistair C. Veitch, 1991, Neural Computation.

Developing data mining applications

Geoff Holmes, 2012, KDD.

Efficient data stream classification via probabilistic adaptive windows

Geoff Holmes, Jesse Read, Albert Bifet, 2013, SAC '13.

An Instrument Control System Using Predictive Modelling

Geoff Holmes, Dale Fletcher, 2004, ICINCO.

Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis

Geoff Holmes, Bernhard Pfahringer, Stefan Mutter, 2008, Australasian Conference on Artificial Intelligence.

Extremely Fast Decision Tree Mining for Evolving Data Streams

Wei Fan, Geoff Holmes, Albert Bifet, 2017, KDD.

Optimizing the Induction of Alternating Decision Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2001, PAKDD.

Classifier chains for multi-label classification

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2009, Machine Learning.

Ensembles of Nested Dichotomies with Multiple Subset Evaluation

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2018, PAKDD.

Big Data with ADAMS

Geoff Holmes, Peter Reutemann, 2015, BigMine.

4 Streams and Sketches

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2018 .

New Options for Hoeffding Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2007, Australian Conference on Artificial Intelligence.

New ensemble methods for evolving data streams

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2009, KDD.

On Calibration of Nested Dichotomies

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2019, PAKDD.

MEKA: A Multi-label/Multi-target Extension to WEKA

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2016, J. Mach. Learn. Res..

Environmental Data Science

Karina Gibert, Geoff Holmes, Jeffery S. Horsburgh, 2018, Environ. Model. Softw..

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..

ONTRACK: Dynamically adapting music playback to support navigation

Matt Jones, Geoff Holmes, David Bainbridge, 2008, Personal and Ubiquitous Computing.

3 Hands-on Introduction to MOA

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2018 .

Generating Rule Sets from Model Trees

Geoff Holmes, Eibe Frank, Mark A. Hall, 1999, Australian Joint Conference on Artificial Intelligence.

MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering

Thomas Seidl, Geoff Holmes, Albert Bifet, 2010, WAPA.

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Geoff Holmes, Jesse Read, Albert Bifet, 2015, Machine Learning.

Stream Data Mining Using the MOA Framework

Thomas Seidl, Geoff Holmes, Jesse Read, 2012, DASFAA.

An effective evaluation measure for clustering on evolving data streams

Thomas Seidl, Geoff Holmes, Albert Bifet, 2011, KDD.

Streaming Data Mining with Massive Online Analytics (MOA)

Geoff Holmes, Jesse Read, Albert Bifet, 2018 .

Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2005, CITA.

Classifier Chains for Multi-label Classification

Geoff Holmes, Jesse Read, Bernhard Pfahringer, 2009, ECML/PKDD.

Multiclass Alternating Decision Trees

Geoff Holmes, Bernhard Pfahringer, Eibe Frank, 2002, ECML.

Using Model Trees for Classification

Yong Wang, Ian H. Witten, Geoff Holmes, 1998, Machine Learning.

Fast Perceptron Decision Tree Learning from Evolving Data Streams

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, PAKDD.

The online performance estimation framework: heterogeneous ensemble learning for data streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2017, Machine Learning.

Active Learning with Evolving Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, ECML/PKDD.

The Positive Effects of Negative Information: Extending One-Class Classification Models in Binary Proteomic Sequence Classification

Geoff Holmes, Bernhard Pfahringer, Stefan Mutter, 2009, Australasian Conference on Artificial Intelligence.

Natural language and computational linguistics - an introduction

Geoff Holmes, Colin Beardon, David Lumsden, 1991, Ellis Horwood Series in computers and their applications.

Towards Meta-learning over Data Streams

Geoff Holmes, Joaquin Vanschoren, Jan N. van Rijn, 2014, MetaSel@ECAI.

Benchmarking Stream Clustering Algorithms within the MOA Framework

Thomas Seidl, Geoff Holmes, Albert Bifet, 2010 .

Leveraging Bagging for Evolving Data Streams

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2010, ECML/PKDD.

On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

Tariq M. Khan, Geoff Holmes, Syed Saud Naqvi, 2019, IEEE Access.

Experiment databases

Geoff Holmes, Hendrik Blockeel, Joaquin Vanschoren, 2012, Machine Learning.

Stress-Testing Hoeffding Trees

Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, 2005, PKDD.

Data provenance in cloud

Geoff Holmes, Bill Rogers, Sivadon Chaisiri, 2017 .

7 Ensemble Methods

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2018 .

Security and Data Accountability in Distributed Systems: A Provenance Survey

Geoff Holmes, Ryan K. L. Ko, Yu Shyang Tan, 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

Digital Libraries Unfurled: Supporting the New Zealand Flag Debate

David M. Nichols, Geoff Holmes, David Bainbridge, 2015, TPDL.

11 Introduction to MOA and Its Ecosystem

Geoff Holmes, Ricard Gavaldà, Francis Bach, 2018 .

Scalable and efficient multi-label classification for evolving data streams

Geoff Holmes, Jesse Read, Albert Bifet, 2012, Machine Learning.

Improving the image recognition capability of Hopfield neural networks

Sally Jo Cunningham, Geoff Holmes, Matthew C. Humphrey, 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

MOA: A Real-Time Analytics Open Source Framework

Thomas Seidl, Geoff Holmes, Jesse Read, 2011, ECML/PKDD.

STRATUS: Towards Returning Data Control to Cloud Users

Gillian Dobbie, Shaoning Pang, Abdolhossein Sarrafzadeh, 2015, ICA3PP.

Analysing chromatographic data using data mining to monitor petroleum content in water

Geoff Holmes, Peter Reutemann, Eibe Frank, 2009, ITEE.

Introduction: special issue of selected papers from ACML 2015

Tie-Yan Liu, Zhi-Hua Zhou, Masashi Sugiyama, 2017, Machine Learning.

II STREAM MINING

Geoff Holmes, Ricard Gavaldà, Albert Bifet, 2018 .

Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA

Thomas Seidl, Geoff Holmes, Albert Bifet, 2010, 2010 IEEE International Conference on Data Mining Workshops.