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Machine Learning （ML） - April 2019, issue 4 论文列表

点击这里查看 Machine Learning 的JCR分区、影响因子等信息
卷期号： April 2019, issue 4
发布时间：
卷期年份： 2019
卷期官网： https://link.springer.com/journal/10994/volumes-and-issues/108-4
本期论文列表
Visualizing and understanding Sum-Product Networks

Improved linear embeddings via Lagrange duality

Continuation methods for approximate large scale object sequencing

The risk of trivial solutions in bipartite top ranking

Unsupervised feature selection based on kernel fisher discriminant analysis and regression learning

Corruption-tolerant bandit learning

2D compressed learning: support matrix machine with bilinear random projections

A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data

Speculate-correct error bounds for k-nearest neighbor classifiers

The kernel Kalman rule

Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes

A greedy feature selection algorithm for Big Data of high dimensionality

Learning rates for kernel-based expectile regression

A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data

Fast generalization rates for distance metric learning

Extreme value correction: a method for correcting optimistic estimations in rule learning

Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

Correction to: Modeling outcomes of soccer matches

Guest editorial: special issue on machine learning for soccer

The Open International Soccer Database for machine learning

Learning to predict soccer results from relational data with gradient boosted trees

Dolores: a model that predicts football match outcomes from all over the world

Modeling outcomes of soccer matches

Incorporating domain knowledge in machine learning for soccer outcome prediction

Probabilistic movement models and zones of control

Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018

Guest editors’ note

Learning efficient logic programs

Semi-supervised online structure learning for composite event recognition

Lifted discriminative learning of probabilistic logic programs

Probabilistic and exact frequent subtree mining in graphs beyond forests

Online probabilistic theory revision from examples with ProPPR

Algorithms for learning parsimonious context trees

Arbitrage of forecasting experts

Constructing effective personalized policies using counterfactual inference from biased data sets with many features

Accelerated gradient boosting

Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion

A simple homotopy proximal mapping algorithm for compressive sensing

Conformal and probabilistic prediction with applications: editorial

Rethinking statistical learning theory: learning using statistical invariants

Online aggregation of unbounded losses using shifting experts with confidence

Nonparametric predictive distributions based on conformal prediction

Majority vote ensembles of conformal predictors

Combination of inductive mondrian conformal predictors

Automatic face recognition with well-calibrated confidence measures

Efficient Venn predictors using random forests

Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018)

Good arm identification via bandit feedback

Supervised representation learning for multi-label classification

Bayesian optimistic Kullback–Leibler exploration

Annotation cost-sensitive active learning by tree sampling

N-ary decomposition for multi-class classification

Millionaire: a hint-guided approach for crowdsourcing

An accelerated variance reducing stochastic method with Douglas-Rachford splitting

Engineering fast multilevel support vector machines

Asymptotically optimal algorithms for budgeted multiple play bandits

Boosting as a kernel-based method

Risk bound of transfer learning using parametric feature mapping and its application to sparse coding

A distributed feature selection scheme with partial information sharing

RankMerging: a supervised learning-to-rank framework to predict links in large social networks

Attentional multilabel learning over graphs: a message passing approach

A Riemannian gossip approach to subspace learning on Grassmann manifold

Distributed Bayesian matrix factorization with limited communication

Collaborative topic regression for predicting topic-based social influence

Dynamic attention-integrated neural network for session-based news recommendation

Correction to: Adaptive random forests for evolving data stream classification

Introduction to the special issue for the ECML PKDD 2019 journal track

Dynamic principal projection for cost-sensitive online multi-label classification

Aggregating Algorithm for prediction of packs

Efficient feature selection using shrinkage estimators

Grouped Gaussian processes for solar power prediction

LSALSA: accelerated source separation via learned sparse coding

Data scarcity, robustness and extreme multi-label classification

Joint detection of malicious domains and infected clients

A flexible probabilistic framework for large-margin mixture of experts

Deep collective matrix factorization for augmented multi-view learning

Temporal pattern attention for multivariate time series forecasting

Compatible natural gradient policy search

TD-regularized actor-critic methods

On PAC-Bayesian bounds for random forests

Efficient learning with robust gradient descent

Nuclear discrepancy for single-shot batch active learning

Improving latent variable descriptiveness by modelling rather than ad-hoc factors

CaDET: interpretable parametric conditional density estimation with decision trees and forests

On the analysis of adaptability in multi-source domain adaptation

The teaching size: computable teachers and learners for universal languages

Distribution-free uncertainty quantification for kernel methods by gradient perturbations

Stochastic gradient Hamiltonian Monte Carlo with variance reduction for Bayesian inference