Bernhard Pfahringer | DeepAI

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Featured Co-authors

Albert Bifet
33 publications
Hongyu Wang
28 publications
Jesse Read
28 publications
Eibe Frank
25 publications
Henry Gouk
25 publications
Luiz S. Oliveira
22 publications
Vithya Yogarajan
9 publications
Andre Gregio
8 publications
Michael Mayo
7 publications
Geoffrey Holmes
7 publications
Yaqian Zhang
7 publications

∙ 09/28/2022

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

Online continual learning (OCL) aims to train neural networks incrementa...

0 Yaqian Zhang, et al. ∙

∙ 05/12/2022

Cross-domain Few-shot Meta-learning Using Stacking

Cross-domain few-shot meta-learning (CDFSML) addresses learning problems...

0 Hongyu Wang, et al. ∙

∙ 12/03/2021

Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents

Multi-label learning predicts a subset of labels from a given label set ...

0 Vithya Yogarajan, et al. ∙

∙ 10/01/2021

Predicting COVID-19 Patient Shielding: A Comprehensive Study

There are many ways machine learning and big data analytics are used in ...

0 Vithya Yogarajan, et al. ∙

∙ 09/02/2021

Semi-Supervised Learning using Siamese Networks

Neural networks have been successfully used as classification models yie...

0 Attaullah Sahito, et al. ∙

∙ 09/02/2021

Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification

Deep neural networks produce state-of-the-art results when trained on a ...

0 Attaullah Sahito, et al. ∙

∙ 09/02/2021

Better Self-training for Image Classification through Self-supervision

Self-training is a simple semi-supervised learning approach: Unlabelled ...

0 Attaullah Sahito, et al. ∙

∙ 01/25/2021

PolyLM: Learning about Polysemy through Language Modeling

To avoid the "meaning conflation deficiency" of word embeddings, a numbe...

0 Alan Ansell, et al. ∙

∙ 10/30/2020

Machine Learning (In) Security: A Stream of Problems

Machine Learning (ML) has been widely applied to cybersecurity, and is c...

39 Fabrício Ceschin, et al. ∙

∙ 05/15/2020

Adaptive XGBoost for Evolving Data Streams

Boosting is an ensemble method that combines base models in a sequential...

0 Jacob Montiel, et al. ∙

∙ 03/29/2020

Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes

Machine learning-based multi-label medical text classifications can be u...

0 Vithya Yogarajan, et al. ∙

∙ 12/26/2019

Classifier Chains: A Review and Perspectives

The family of methods collectively known as classifier chains has become...

0 Jesse Read, et al. ∙

∙ 01/27/2019

Automatic end-to-end De-identification: Is high accuracy the only metric?

De-identification of electronic health records (EHR) is a vital step tow...

0 Vithya Yogarajan, et al. ∙

∙ 01/23/2019

Stochastic Gradient Trees

We present an online algorithm that induces decision trees using gradien...

0 Henry Gouk, et al. ∙

∙ 10/16/2018

A survey of automatic de-identification of longitudinal clinical narratives

Use of medical data, also known as electronic health records, in researc...

0 Vithya Yogarajan, et al. ∙

∙ 09/08/2018

On the Calibration of Nested Dichotomies for Large Multiclass Tasks

Nested dichotomies are used as a method of transforming a multiclass cla...

0 Tim Leathart, et al. ∙

∙ 09/08/2018

Ensembles of Nested Dichotomies with Multiple Subset Evaluation

A system of nested dichotomies is a method of decomposing a multi-class ...

0 Tim Leathart, et al. ∙

∙ 07/31/2018

Probability Calibration Trees

Obtaining accurate and well calibrated probability estimates from classi...

0 Tim Leathart, et al. ∙

∙ 04/16/2018

MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes

Effective regularisation of neural networks is essential to combat overf...

0 Henry Gouk, et al. ∙

∙ 04/12/2018

Regularisation of Neural Networks by Enforcing Lipschitz Continuity

We investigate the effect of explicitly enforcing the Lipschitz continui...

0 Henry Gouk, et al. ∙

∙ 04/07/2016

Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection

A system of nested dichotomies is a method of decomposing a multi-class ...

0 Tim Leathart, et al. ∙

∙ 11/19/2015

Fast Metric Learning For Deep Neural Networks

Similarity metrics are a core component of many information retrieval an...

0 Henry Gouk, et al. ∙

∙ 04/23/2015

Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams

In this research, we apply ensembles of Fourier encoded spectra to captu...

0 Sripirakas Sakthithasan, et al. ∙

∙ 10/19/2012

Locally Weighted Naive Bayes

Despite its simplicity, the naive Bayes classifier has surprised machine...

0 Eibe Frank, et al. ∙

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