Safe density ratio modeling

Time Series Prediction via Density Ratio Modeling

Dragging: Density-Ratio Bagging

We propose density-ratio bagging (dragging), a semi-supervised extension of bootstrap aggregation (bagging) method. Additional unlabeled training data are used to calculate the weight on each labeled training point by a density-ratio estimator. The weight is then used to construct a weighted labeled empirical distribution, from which bags of bootstrap samples are drawn. Asymptotically, dragging...

Discriminative Density-ratio Estimation

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques ...

Density Ratio Hidden Markov Models

Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing. Being generative rather than discriminative models, however, their classification performance is a drawback. In this paper we apply ideas from the field of density ratio estimation to bypass the difficult step of lear...

Modeling human population patterns on tree density

In order to evaluate the possible correlation between the tree density and the human population density, the forested area in Nav Asalem district located in Guilan Province was selected. The descriptors of tree number and basal area per hectare as well as the stand density index were used to determine the tree density, which was conducted from a 2014 forest inventory including 62 cluster (558 p...