scispace - formally typeset
JournalISSN: 1532-4435

Journal of Machine Learning Research

About: Journal of Machine Learning Research is an academic journal. The journal publishes majorly in the area(s): Support vector machine & Kernel (statistics). It has an ISSN identifier of 1532-4435. Over the lifetime, 3144 publication(s) have been published receiving 519320 citation(s). The journal is also known as: JMLR & J Mach Learn Res. more


Open accessJournal Article
Abstract: 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 machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from more

33,540 Citations

Open accessJournal Article
Abstract: Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. more

Topics: Overfitting (66%), Deep learning (62%), Convolutional neural network (61%) more

27,534 Citations

Open accessJournal ArticleDOI: 10.5555/944919.944937
Abstract: 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 over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. more

27,392 Citations

Open accessJournal Article
Abstract: 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 in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets. more

Topics: Sammon mapping (58%), t-distributed stochastic neighbor embedding (57%), Isomap (57%) more

22,120 Citations

Open accessJournal ArticleDOI: 10.1162/153244303322753616
Isabelle Guyon, André Elisseeff1Institutions (1)
Abstract: 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 text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods. more

13,554 Citations

No. of papers from the Journal in previous years

Top Attributes

Show by:

Journal's top 5 most impactful authors

Tong Zhang

18 papers, 4.2K citations

Francis Bach

15 papers, 4.4K citations

Martin J. Wainwright

14 papers, 2.2K citations

Michael I. Jordan

14 papers, 3.4K citations

Chih-Jen Lin

12 papers, 11.7K citations

Network Information
Related Journals (5)
arXiv: Machine Learning

12.4K papers, 260.6K citations

95% related
arXiv: Learning

45K papers, 837.1K citations

92% related
Machine Learning

2.1K papers, 314.2K citations

89% related
arXiv: Statistics Theory

11.8K papers, 156.5K citations

87% related
Electronic Journal of Statistics

1.4K papers, 28.5K citations

87% related