Forecasting Surface O3 in Texas Urban Areas Using Random Forest and Generalized Additive Models

Random Projection Features and Generalized Additive Models

We propose to learn generalized additive models for classification which represents the classifier using a sum of piecewise linear functions and show that a recently proposed fast linear SVM training method (Pegasos) can be adapted to train such models with the same convergence rates. To be able to learn functions on combination of dimensions, we explore the use of random projection features wh...

Development of Accident Modification Factors for Rural Frontage Road Segments in Texas Using Generalized Additive Models

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedi...

Model-assisted Estimation of Forest Resources with Generalized Additive Models

Multi-phase surveys are often conducted in forest inventory, with the goal of estimating forested area and tree characteristics over large regions. This article describes how design-based estimation of such quantities, based on information gathered during ground visits of sampled plots, can be made more precise by incorporating auxiliary information available from remote sensing. The relationsh...

Using generalized additive models and random forests to model prosodic prominence in German

The perception of prosodic prominence is influenced by different sources like different acoustic cues, linguistic expectations and context. We use a generalized additive model and a random forest to model the perceived prominence on a corpus of spoken German. Both models are able to explain over 80% of the variance. While the random forests give us some insights on the relative importance of th...