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Journal of Machine Learning Research

Known as: JMLR 
The Journal of Machine Learning Research (usually abbreviated JMLR), is a scientific journal focusing on machine learning, a subfield of artificial… 
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Related topics

Related topics

10 relations
Adversarial machine learningAkaike information criterionArtificial intelligenceBregman divergence

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Syndromic Surveillance using Generic Medical Entities on Twitter
Public health surveillance is challenging due to difficulties accessing medical data in real-time. We present a novel, effective… 
2015
2015
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains
Many machine learning methods are given as parameterized optimization problems. Important examples of such parameters are… 
2015
2015
The Information Sieve
We introduce a new framework for unsupervised learning of representations based on a novel hierarchical decomposition of… 
2015
2015
Visualizing User Model in Exploratory Search Tasks
We present our ongoing work on an interactive exploratory information retrieval system designed to explain to the user the… 
2015
2015
Clustered Sparse Bayesian Learning
Many machine learning and signal processing tasks involve computing sparse representations using an overcomplete set of features… 
2013
2013
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models
In this study, we derive algorithms for estimating mixed β-divergences. Such cost functions are useful for Nonnegative Matrix and… 
2011
2011
Relational Learning with One Network: An Asymptotic Analysis
Theoretical analysis of structured learning methods has focused primarily on domains where the data consist of independent… 
2011
2011
Catégorisation par mesures de dissimilitude et caractérisation d'images en multi échelle
Dans cette these, on introduit la metrique "Coefficient de forme" pour la classement des donnees de dissimilitudes. Cette… 
2008
2008
Efficient Bayesian methods for clustering.
One of the most important goals of unsupervised learning is to discover meaningful clusters in data. Clustering algorithms strive… 
2006
2006
SHOGUN - A Large Scale Machine Learning Toolbox
We have developed an R Interface for our Machine Learning Toolbox SHOGUN. It features algorithms to train hidden markov models… 
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