DEI

Orador Convidado: Carlo Gatta (Investigador do Centro de Visão Computacional - CVC - da Universidade de Barcelona)

Titulo: "Multiscale Stacked Sequential Learning"

Abstract: The classification and/or segmentation of sequential data (such as text, audio, images and videos) can be improved by moving from i.i.d. classifiers to contextual-aware methods. One popular approach to contextual-aware classification is the Conditional Random Fields (CRF), or other related graph-based algorithms. In this seminar we will show a method, called “Multi-scale Stacked Sequential Learning” (MSSL), which allows considering the context in a very efficient way. The method proved to outperform CRFs in several tasks, in terms of accuracy, precision, sensitivity. Training and testing times are more than one order smaller than CRFs. We tested the method on text classification, image segmentation (binary and 8 classes problems), temporal classification, and volumetric classification. Moreover, its implementation is straightforward (less than half an our in MATLAB and <100 lines of code) and, being a meta-learning scheme, any classifier can be employed in its use (kNN, Adaboost, SVMs, etc…). The meta-learner MATLAB code will be provided to the audience.