This work investigates the use of Random Forests for class based pixel-wise segmentation of images. The contribution of this paper is three-fold. First, we show that apparently quite dissimilar classifiers (such as nearest neighbour matching to texton class histograms) can be mapped onto a Random Forest architecture. Second, based on this insight, we show that the performance of such classifier...

In the paper the comparison of ensemble based methods applied to censored survival data was conducted. Bagging survival trees, dipolar survival tree ensemble and random forest were taken into consideration. The prediction ability was evaluated by the integrated Brier score, the prediction measure developed for survival data. Two real datasets with different percentage of censored observations w...

Let us suppose that in a result of some action in a random time X we get a random receipt Y. If actions are repeated one-by-one, then in the time interval [0, t] we are interested in the cumulative process of receipts. Let (X, Y) and (Xn, Yn), with n ! 1, be independent equidistributed random vectors. Let X be a positive random variable and Y be a nonnegative integer random variable. We assume ...

In this work, we expose four bijections each allowing to increase (or decrease) one parameter in either uniform random forests with a fixed number of edges and trees, or quadrangulations with a boundary having a fixed number of faces and a fixed boundary length. In particular, this gives a way to sample a uniform quadrangulation with n + 1 faces from a uniform quadrangulation with n faces or a ...

Where did we lose in this argument? First, whenever you do a union bound, you’re overestimating the probability of the bad event. In particular, this can be a gross overestimate if many labelings are similar to each other, because then if the random swapping of one pair is unlikely to cause harm, then swapping another close one is unlikely to cause harm. Next lecture, we will find a way to get ...

This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events using random forest regression. The audio signals are decomposed into multiple densely overlapping superframes annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using the displacement information, a multi...

Random forests are a very effective and commonly used statistical method, but their full theoretical analysis is still an open problem. As a first step, simplified models such as purely random forests have been introduced, in order to shed light on the good performance of random forests. In this paper, we study the approximation error (the bias) of some purely random forest models in a regressi...

In our previous two installments on innovativeness we provided overviews of how forest industry managers define “innovative” companies and what they see as primary hurdles to innovativeness. Here we cover the remaining findings regarding how managers might measure innovativeness and what they do to proactively increase innovativeness within their firms. Findings in this area may be particularly...

We combine random forest (RF) and conditional random field (CRF) into a new computational framework, called random forest random field (RF). Inference of (RF) uses the Swendsen-Wang cut algorithm, characterized by MetropolisHastings jumps. A jump from one state to another depends on the ratio of the proposal distributions, and on the ratio of the posterior distributions of the two states. Prior...