Skip to search formSkip to main contentSkip to account menu

MultiBoosting: A Technique for Combining Boosting and Wagging

TLDR
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees that is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction.
View on Springer (opens in a new tab)

Not So Naive Bayes: Aggregating One-Dependence Estimators

TLDR
A new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers is presented, which delivers comparable prediction accuracy to LBR and Super-Parent TAN with substantially improved computational efficiency at test timerelative to the former and at training time relative to the latter.
View on Springer (opens in a new tab)

InceptionTime: Finding AlexNet for Time Series Classification

TLDR
An important step towards finding the AlexNet network for TSC is taken by presenting InceptionTime---an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture, which outperforms HIVE-COTE's accuracy together with scalability.
View PDF on arXiv (opens in a new tab)

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

TLDR
This paper shows that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods for time series classification.
View on Springer (opens in a new tab)

Encyclopedia of Machine Learning

TLDR
The style of the entries in the Encyclopedia of Machine Learning is expository and tutorial, making the book a practical resource for machine learning experts, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.
View via Publisher (opens in a new tab)

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

TLDR
It is shown that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision.
View via Publisher (opens in a new tab)

NMR of proteins and nucleic acids

This report highlights key papers, published this year, that describe advances in the development and application of NMR techniques to the study of proteins and nucleic acids. Many of the sections…
View via Publisher (opens in a new tab)

Lazy Learning of Bayesian Rules

TLDR
This paper proposes the application of lazy learning techniques to Bayesian tree induction and presents the resulting lazy Bayesian rule learning algorithm, called LBR, which can be justified by a variant of Bayes theorem which supports a weaker conditional attribute independence assumption than is required by naive Bayes.
View on Springer (opens in a new tab)

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

TLDR
The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification, and some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size are provided.
View PDF on arXiv (opens in a new tab)

Discovering Significant Patterns

TLDR
This paper proposes techniques to overcome the extreme risk of type-1 error by applying well-established statistical practices, which allow the user to enforce a strict upper limit on the risk of experimentwise error.
View on Springer (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)