The value of an American option is the maximized discounted cash flows from option. At each time step, one needs to compare immediate exercise with continuation and decide as soon strictly greater than value. We can formulate this problem a dynamic programming equation, where main difficulty comes computation conditional expectations representing values at step. In (Longstaff Schwartz, 2001), t...

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Random forests have emerged as one of the most commonly used nonparametric statistical methods in many scientific areas, particularly in analysis of high throughput genomic data. A general practice in using random forests is to generate a sufficiently large number of trees, although it is subjective as to how large is sufficient. Furthermore, random forests are viewed as "black-box" because of ...

We investigate the use of decision forests for automated e-mail filing into folders and junk e-mail filtering. The experiments show that decision forests offer the following advantages: (i) ability to deal with the large dimensionality of feature vectors in text categorization, (ii) improved accuracy of the ensemble over the single decision trees and favourable comparison with a number of other...

in order to evaluate capability of the landsat-etm and irs-p6-liss iv images for canopy cover mapping a case study was done on the forests of javanroud in kermanshah province. after evaluation of the geometric and radiometric quality of the data, the etm+ images, the etm+ images were geometrically corrected with gcps and the images were registered with rmse error 0.46 and 0.48 pixels, for x and...

Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken ...

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. Experiments results show that our proposed algorithm beats the original random forest and other popular classification algorithms such as SVM, KNN and C4.5 in t...