ProfessorsProfessors of University of AucklandThomas Lacombe

Thomas Lacombe

University of Auckland

H-index: 5

Oceania-New Zealand

About Thomas Lacombe

Thomas Lacombe, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at University of Auckland, specializes in the field of Machine learning, Image Processing, Computer Vision, Data Streams, Drift Detection.

His recent articles reflect a diverse array of research interests and contributions to the field:

Interpretability Meets Generalizability: A Hybrid Machine Learning System to Identify Nonlinear Granger Causality in Global Stock Indices

Cost-effective transfer learning for data streams

Probabilistic exact adaptive random forest for recurrent concepts in data streams

Transfer learning with adaptive online tradaboost for data streams

Nacre: Proactive recurrent concept drift detection in data streams

A meta-learning approach for automated hyperparameter tuning in evolving data streams

Pearl: Probabilistic exact adaptive random forest with lossy counting for data streams

Polarimetric imaging for quality control in injection molding

Thomas Lacombe Information

University	University of Auckland
Position	- Computer science department
Citations(all)	57
Citations(since 2020)	53
Cited By	11
hIndex(all)	5
hIndex(since 2020)	5
i10Index(all)	0
i10Index(since 2020)	0
Email	Access Email
University Profile Page	University of Auckland
Google Scholar	View Google Scholar Profile

Thomas Lacombe Skills & Research Interests

Machine learning

Image Processing

Computer Vision

Data Streams

Drift Detection

Top articles of Thomas Lacombe

Title	Journal	Author(s)	Publication Date
Interpretability Meets Generalizability: A Hybrid Machine Learning System to Identify Nonlinear Granger Causality in Global Stock Indices		Yixiao Lu Yokiu Lee Haoran Feng Johnathan Leung Alvin Cheung ...	2023/5/25
Cost-effective transfer learning for data streams		Ocean Wu Yun Sing Koh Gillian Dobbie Thomas Lacombe	2022/11/28
Probabilistic exact adaptive random forest for recurrent concepts in data streams	International Journal of Data Science and Analytics	Ocean Wu Yun Sing Koh Gillian Dobbie Thomas Lacombe	2022/1
Transfer learning with adaptive online tradaboost for data streams		Ocean Wu Yun Sing Koh Gillian Dobbie Thomas Lacombe	2021/11/28
Nacre: Proactive recurrent concept drift detection in data streams		Ocean Wu Yun Sing Koh Gillian Dobbie Thomas Lacombe	2021/7/18
A meta-learning approach for automated hyperparameter tuning in evolving data streams		Thomas Lacombe Yun Sing Koh Gillian Dobbie Ocean Wu	2021/7/18
Pearl: Probabilistic exact adaptive random forest with lossy counting for data streams		Ocean Wu Yun Sing Koh Gillian Dobbie Thomas Lacombe	2020
Polarimetric imaging for quality control in injection molding	Journal of Electronic Imaging	Pierre Nagorny Thomas Lacombe Hugues Favreliere Maurice Pillet	2020/8
Modal features for image texture classification	Pattern Recognition Letters	Thomas Lacombe Hugues Favreliere Maurice Pillet	2020/7/1