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...

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...

Most if not all survival analysis approaches are focused on survival (or time-to-event) outcomes, which are usually associated with serious disease conditions, such as death, heart failures and recurrence of cancers. Typically, survival outcome includes one binary variable for occurrence(s) of the event(s) of interest and at least one continuous variable for the time of the occurrence(s) or the...