Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such a...

We propose a new parallelized high-dimensional single-query path planning technique that uses a coupled forest of random trees (i.e., instead of a single tree). We present both theoretical and experimental results that show using forests of random trees can lead to expected super linear speedup, with respect to the number of trees in the forest. In other words, with T trees running in parallel,...

An algorithm for the localization and counting of cells in histopathological images is presented. The algorithm relies on the presegmentation of an image into a number of superpixels followed by two random forests for classification. The first random forest determines if there are any cells in the superpixels at its input and the second random forest provides the number of cells in the respecti...

Private companies backed by venture capitalists or private equity funds receive their funding in a series of rounds. Information about when each round occurred and which investors participated in each round has been compiled into different databases. Here we mine one such database to model how the private company will exit the VC/PE space. More specifically, we apply a random forest algorithm t...

We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure or origin of the ensemble, without parameters, and as scalably as linear lea...

This paper proposes the use of data available at Manchester Metropolitan University to assess the variables that can best predict student progression. We combine Virtual Learning Environment and MIS student records data sets and apply the Random Forest (RF) algorithm to ascertain which variables can best predict students’ progression (students satisfactorily completing one year and passing to t...

We present a simple strong refutation algorithm for random k-SAT formulas. Our algorithm applies to random k-SAT formulas on n variables with ω(n)n(k+1)/2 clauses for any ω(n) → ∞. In contrast to the earlier results of Coja-Oghlan, Goerdt, and Lanka (for k = 3, 4) and Coja-Oghlan, Cooper, and Frieze (for k ≥ 5), which address the same problem for even sparser formulas our algorithm is more elem...

This article describes a system that automatically recognizes individual pitch types like screwballs and sliders in baseball broadcast videos. These decisions are currently made by human specialists in baseball, who are watching the broadcast video of the game. No automatic system has yet been developed for identifying individual pitch types from single view camera images. Techniques using mult...