Encyclopedia of Machine Learning

@inproceedings{Sammut2010EncyclopediaOM,
  title={Encyclopedia of Machine Learning},
  author={Claude Sammut and Geoffrey I. Webb},
  booktitle={Encyclopedia of Machine Learning},
  year={2010}
}
This comprehensive encyclopedia, with over 250 entries in an A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of machine learning. Most entries in this preeminent work include useful literature references.Topics for the Encyclopedia of Machine Learning were selected by a distinguished international advisory board. These peer-reviewed, highly-structured entries include definitions, illustrations, applications, bibliographies… 

Binarised regression tasks: methods and evaluation metrics

TLDR
A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.

Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods

TLDR
Questions of interest to prospective users of supervised learning methods, which are used to automatically classify events based on a pre-existing classification system, are considered in the context of a particular dataset—the Congressional Bills Project—which includes more than 400,000 bill titles that humans have classified into 20 policy topics.

Species determination using AI machine-learning algorithms:

TLDR
An Artificial Intelligence (AI) machine-learning species identifier has been developed that takes as input locality data and a small number of the morphological parameters and was able to identify 77% correctly with its highest probabilistic match and over 99% of collections within its five most likely determinations.

ISART: A Generic Framework for Searching Books with Social Information

TLDR
Experiments show that this technology permits embedding social information to promote book search effectiveness, and IsArt, by making use of it, has the best performance on CLEF/INEX Social Book Search Evaluation datasets of all 4 years, compared with some other state-of-the-art methods.

The Shape of Learning Curves: a Review

TLDR
This review recounts the origins of the term, provides a formal definition of the learning curve, and provides a comprehensive overview of the literature regarding the shape of learning curves.

LazyBum: Decision tree learning using lazy propositionalization

TLDR
LazyBum is presented, a system that can be considered a lazy version of the recently proposed OneBM method for propositionalization, and achieves a comparable accuracy with a lower execution time on most of the datasets.

Feature selection methods for text classification: a systematic literature review

TLDR
A Systematic Literature Review that asses 1376 unique papers from journals and conferences published in the past eight years (2013–2020), and helps researchers to develop and position new studies with respect to the existing literature.

Learning and Inference for Structured Prediction: A Unifying Perspective

TLDR
A unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses is presented.

Bayesian nonparametric learning for complicated text mining

TLDR
There is still a gap between the Bayesian nonparametric learning theory and complicated real-world applications and its ability to conduct complicated text mining tasks, such as: document-word co-clustering, document network learning, multi-label document learning, and so on, is still weak.

Expertise-aware news feed updates recommendation: a random forest approach

TLDR
Following extensive experiments on a real dataset crawled from Twitter, the results show that infer the author’s expertise is critical for identifying relevant updates in news feeds.
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