ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudolikelihood. In this paper, we ...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and pra...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In ...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
We consider feature extraction (dimensionality reduction) for compositional data, where the data vectors are constrained to be positive and constant-sum. In real-world problems, t...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...