Sciweavers

ML
2015
ACM
8 years 25 days ago
Asymptotic analysis of the learning curve for Gaussian process regression
Abstract This paper deals with the learning curve in a Gaussian process regression framework. The learning curve describes the generalization error of the Gaussian process used for...
Loic Le Gratiet, Josselin Garnier
ML
2015
ACM
8 years 25 days ago
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases
Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent and highly expressive SRL model is M...
Tushar Khot, Sriraam Natarajan, Kristian Kersting,...
ML
2015
ACM
8 years 25 days ago
Probabilistic clustering of time-evolving distance data
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the i...
Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir ...
ML
2015
ACM
8 years 25 days ago
Bandit-based Monte-Carlo structure learning of probabilistic logic programs
Probabilistic logic programming allows to model domains with complex and uncertain relationships among entities. While the problem of learning the parameters of such programs has b...
Nicola Di Mauro, Elena Bellodi, Fabrizio Riguzzi
ML
2015
ACM
8 years 25 days ago
Lifted graphical models: a survey
Angelika Kimmig, Lilyana Mihalkova, Lise Getoor
ML
2015
ACM
8 years 25 days ago
Direct conditional probability density estimation with sparse feature selection
Regression is a fundamental problem in statistical data analysis, which aims at estimating the conditional mean of output given input. However, regression is not informative enoug...
Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
ML
2015
ACM
8 years 25 days ago
Greedy learning of latent tree models for multidimensional clustering
Tengfei Liu, Nevin Lianwen Zhang, Peixian Chen, Ap...
ML
2015
ACM
8 years 25 days ago
Unconfused ultraconservative multiclass algorithms
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago by, e.g. Bylan...
Ugo Louche, Liva Ralaivola