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Journal of Machine Learning Research is a peer-reviewed journal published by Journal of Machine Learning Research, covering Neural Networks and Applications, Machine Learning and Algorithms, Face and Expression Recognition. Over its lifetime it has published 1,411 papers, which have received 167,110 citations.
| Publisher | Journal of Machine Learning Research |
|---|---|
| Reference style | Author–year (natbib) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Journal of Machine Learning Research, 12(3), pp. 45–58.
Formats any DOI in Journal of Machine Learning Research style. No sign-up. |
| Publishes research in | Neural Networks and Applications Machine Learning and Algorithms Face and Expression Recognition Statistical Methods and Inference Sparse and Compressive Sensing Techniques |
| ISSN | 1532-4435 |
| h-index | 132 |
| i10-index | 652 |
| Top institutions publishing here | Princeton University |
| Journal website | www.jmlr.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together…
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture…
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing mach...
Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include t...
The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Mahalanobis distance metric for kNN classification from labeled examples. The Mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes…
Transactions on Machine Learning Research
IEEE Transactions on Big Data
IEEE Transactions on Broadcasting
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Communications
IEEE Transactions on Computational Social Systems
IEEE Transactions on Control of Network Systems
IEEE Transactions on Device and Materials Reliability
