We address the problem of separating two audio sources from a single channel mixture recording. A novel method called Multi Layered Random Forest (MLRF) that learns a binary mask for both the sources is presented. Random Forest (RF) classifiers are trained for each frequency band of a source spectrogram. A specialized set of linear transformations are applied to a local time-frequency (T-F) nei...

We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary ...

A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI-TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF...

In this paper, we present a machine learning approach based on random forest (RF) for automatic segmentation of living cells in phase contrast images. The proposed method is performed by a multistage classification working on both low and high level of the image. Pixel-wise classification is first performed to obtain a probability map of dark and bright cell regions. K-means clustering is then ...

This paper presents an efficient algorithm for face recognition using the local binary pattern (LBP) and random forest (RF). The novelty of this research effort is that a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of facial texture classification performance, is applied. Furthermore, RF is used to select the most important featu...

In this paper we show that the Random Forest (RF) approach can be successfully implemented for language modeling of an inflectional language for Automatic Speech Recognition (ASR) tasks. While Decision Trees (DTs) perform worse than a conventional trigram language model (LM), RFs outperform the latter. WER (up to 3.4% relative) and perplexity (10%) reduction over the trigram model can be gained...

In this paper we present an experimental study of the performance of three machine learning algorithms applied to the difficult problem of galaxy classification. We use the Naive Bayes classifier, the rule-induction algorithm C4.5 and a recently introduced classifier named random forest (RF). We first employ image processing to standardize the images, eliminating the effects of orientation and ...

This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R(2) (pred) of 0.72, a standard error of prediction (SEP) of 0.45, for an external prediction set of 51...