Machine Learning（ML ）的JULY 2015, ISSUE 1论文列表

卷期号： July 2015, issue 1

发布时间：

卷期年份： 2015

卷期官网： https://link.springer.com/journal/10994/volumes-and-issues/100-1

Guest editors’ introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning

Probabilistic (logic) programming concepts

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

Efficient inference and learning in a large knowledge base

Bandit-based Monte-Carlo structure learning of probabilistic logic programs

Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track

Direct conditional probability density estimation with sparse feature selection

Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge

Learning relational dependency networks in hybrid domains

Policy gradient in Lipschitz Markov Decision Processes

A Bayesian approach for comparing cross-validated algorithms on multiple data sets

Soft-max boosting

Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions

Consensus hashing

Generalized Twin Gaussian processes using Sharma–Mittal divergence

Improving classification performance through selective instance completion

Optimised probabilistic active learning (OPAL)

Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data

A decomposition of the outlier detection problem into a set of supervised learning problems

Convex relaxations of penalties for sparse correlated variables with bounded total variation

Incremental learning of event definitions with Inductive Logic Programming

Generalized gradient learning on time series

Learning from evolving video streams in a multi-camera scenario

Probabilistic clustering of time-evolving distance data

Regularized feature selection in reinforcement learning

Half-space mass: a maximally robust and efficient data depth method