Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. How-ever, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scal-able, cost-efficient and systematic approaches for conserva-tion planning is an important and challenging computational task. In particular, preserving landscape connectivity be-tween good habitat has become a key conservation priority in recent years. We give an overview of landscape connectiv-ity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator ca-pable of creating families of randomized structured problems,

and prediction with large incomplete and heterogeneous datasets

This study compares the performance of sign detection based on synthetic training data to the performance of detection based on real-world training images. Viola-Jones detectors are created for 4 different traffic signs with both synthetic and real data, and varying numbers of training samples

Abstract. This paper illustrates how a combination of information extraction, machine learning, and NLP corpus annotation practice was applied to a problem of ranking vulnerability of structures (service boxes, manholes) in the Manhattan electrical grid. By adapting NLP corpus annotation methods to the task of knowledge transfer from domain experts, we compensated for the lack of operational definitions of components of the model, such as serious event. The machine learning depended on the ticket classes, but it was not the end goal. Rather, our rule-based document classification determines both the labels of examples and their feature representations. Changes in our classification of events led to improvements in our model, as reflected in the AUC scores for the full ranked list of over 51K structures. The improvements for the very top of the ranked list, which is of most importance for prioritizing work on the electrical grid, affected one in every four or five structures. 1

tionally on both simulated and real world datasets.

to the machine learning community. There are in the literature some indications that very simple rules may achieve surprisingly high accuracy on many datasets. For example, Rendell occasionally remarks that many real world datasets have "few peaks (often just one) " and so are &

), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment -- over half a million runs of C4.5 and a Naive-Bayes algorithm -- to estimate the effects of different parameters on these algorithms on real-world datasets. For cross

Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several

in the case of classification and clustering, model accuracy in the case of segmentation) offer an amount of "improvement" that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets, or the variance that would have been observed

A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much