Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical machine-learning-based approaches proposed to construct effective models, but with limited success due spatial biases in presences imbalanced presence-absences. We propose a novel model...

In TRECVID 2007 high-level feature (HLF) detection, we extend the well-known LIBSVM and develop a toolkit specifically for HLF detection. The package shortens the learning time and provides a framework for researchers to easily conduct experiments. We efficiently and effectively aggregate detectors of training past data to achieve better performances. We propose post-processing techniques, conc...

Phase I drug-combination trials are becoming commonplace in oncology. Most of the current dose-finding designs aim to quantify the toxicity probability space using certain prespecified yet complicated models. These models need to characterize not only each individual drug’s toxicity profile, but also their interaction effects, which often leads to multi-parameter models. We propose a novel Baye...

In this work we apply mixed ensemble models in order to build a classifier for the Ford Classification Challenge. We build feature vectors from the data sequences in terms of first order statistics, spectral density and autocorrelation. Our model selection scheme is a mixture of cross-validation and bagging. The outcome is an ensemble model, that consits of several different models trained on r...

We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use this for forecasting. We investigate the distributional properties of a number of different schemes for model choice and parameter estimation: in-sample model selection using the Akaike information criterion, out-of-sample model selection, an...

Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. We extend the definition of bagging from statistics to statistical functionals and study the von Mises expansion of bagged ...

The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochasti...