Automatic Segmentation and Identification of Spinous Processes on Sagittal X-Rays Based on Random Forest Classification and Dedicated Contextual Features

A class of non homogeneous self interacting random processes with applications to learning in games and vertex-reinforced random walks... hal-00284649, version 1 - 4

In this paper, we take advantages of two machine learning approaches, gradient boosting and random Fourier features, to derive a novel algorithm that jointly learns a

Four different classifiers, Support vector machine (SVM), Random forest (RF), Logistic regression (LR) and k-Nearest Neighbour (k-NN) were applied to classify colour and

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

We formulate our problem as a labeling problem, that we will solve by minimizing a first-order Markov Random Fields (MRF) energy function. In this framework, the nodes in V are

In this paper, we propose a more general framework which does not rely upon pre-processing and segmentation of the lesions and is based on sparse coded features and Random Forests

As shown in the above algorithm, the main difference between Forest-RI and Forest-RK lies in that K is randomly chosen for each node of the tree leading therefore to more diverse

combining the view-specific dissimilarities with a dynamic combination, for which the views are solicited differently from one instance to predict to another; this dynamic