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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

Data mining and knowledge discovery, 2020-09, Vol.34 (5), p.1454-1495

2020

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iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Nucleic acids research, 2021-06, Vol.49 (10), p.e60-e60

2021

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InceptionTime: Finding AlexNet for time series classification

Data mining and knowledge discovery, 2020-11, Vol.34 (6), p.1936-1962

2020

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Proximity Forest: an effective and scalable distance-based classifier for time series

Data mining and knowledge discovery, 2019-05, Vol.33 (3), p.607-635

2019

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SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting

Machine learning, 2023-07, Vol.112 (7), p.2555-2591

2023

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PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

Journal of theoretical biology, 2018-04, Vol.443, p.125-137

2018

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A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

IEEE/ACM transactions on computational biology and bioinformatics, 2021-09, Vol.18 (5), p.1801-1810

2021

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Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Machine learning, 2012-02, Vol.86 (2), p.233-272

2012

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MultiRocket: multiple pooling operators and transformations for fast and effective time series classification

Data mining and knowledge discovery, 2022-09, Vol.36 (5), p.1623-1646

2022

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A novel selective naïve Bayes algorithm

Knowledge-based systems, 2020-03, Vol.192, p.105361, Article 105361

2020

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Layered critical values: a powerful direct-adjustment approach to discovering significant patterns

Machine learning, 2008-06, Vol.71 (2-3), p.307-323

Webb, Geoffrey I.

2008

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TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Data mining and knowledge discovery, 2020-05, Vol.34 (3), p.742-775

2020

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Characterizing concept drift

Data mining and knowledge discovery, 2016-07, Vol.30 (4), p.964-994

2016

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A tutorial on statistically sound pattern discovery

Data mining and knowledge discovery, 2019-03, Vol.33 (2), p.325-377

2019

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Filtered-top-k association discovery

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2011-05, Vol.1 (3), p.183-192

Webb, Geoffrey I.

2011

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Hydra: competing convolutional kernels for fast and accurate time series classification

Data mining and knowledge discovery, 2023-09, Vol.37 (5), p.1779-1805

2023

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Discovering significant patterns

Machine learning, 2007-07, Vol.68 (1), p.1-33

WEBB, Geoffrey I

2007

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Early abandoning and pruning for elastic distances including dynamic time warping

Data mining and knowledge discovery, 2021-11, Vol.35 (6), p.2577-2601

2021

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

Machine learning, 2023-06, Vol.112 (6), p.1941-1973

2023

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Survey of distance measures for quantifying concept drift and shift in numeric data

Knowledge and information systems, 2019-08, Vol.60 (2), p.591-615

2019

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An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams

IEEE transactions on fuzzy systems, 2020-07, Vol.28 (7), p.1315-1328

2020

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Time series extrinsic regression: Predicting numeric values from time series data

Data mining and knowledge discovery, 2021, Vol.35 (3), p.1032-1060

2021

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MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-Binding Sites Using a Random Forest Algorithm with Two- Step Feature Selection

Current bioinformatics, 2017-12, Vol.12 (6)

2017

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Analyzing concept drift and shift from sample data

Data mining and knowledge discovery, 2018-09, Vol.32 (5), p.1179-1199

2018

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Discretization for naive-Bayes learning: managing discretization bias and variance

Machine learning, 2009, Vol.74 (1), p.39-74

2009

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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

InceptionTime: Finding AlexNet for time series classification

Proximity Forest: an effective and scalable distance-based classifier for time series

SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting

PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework

A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

MultiRocket: multiple pooling operators and transformations for fast and effective time series classification

A novel selective naïve Bayes algorithm

Layered critical values: a powerful direct-adjustment approach to discovering significant patterns

TS-CHIEF: a scalable and accurate forest algorithm for time series classification

Characterizing concept drift

A tutorial on statistically sound pattern discovery

Filtered-top-k association discovery

Hydra: competing convolutional kernels for fast and accurate time series classification

Discovering significant patterns

Early abandoning and pruning for elastic distances including dynamic time warping

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

Survey of distance measures for quantifying concept drift and shift in numeric data

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Nonstationary Data Streams

Time series extrinsic regression: Predicting numeric values from time series data

MetalExplorer, a Bioinformatics Tool for the Improved Prediction of Eight Types of Metal-Binding Sites Using a Random Forest Algorithm with Two- Step Feature Selection

Analyzing concept drift and shift from sample data

Discretization for naive-Bayes learning: managing discretization bias and variance

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