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We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...
In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...
Even if models are able to predict more and more accurately pollutant discharge in streams, surface water sampling remains a very common practice to monitor substance concentrations and loads in streams and to calibrate models. However, as this method is t ...
Within the framework of the EU FP6 Project ARCHES, improved Ultra High Performance Fibre Reinforced Concretes (UHPFRC) based on local components were developed and applied to the rehabilitation of an 36 years old reinforced concrete bridge in Slovenia. Two ...
Sample preparation for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) via a microfluidic deposition device using ionic liquid matrices addresses several problems of standard protocols with crystalline matrices, such as the heterog ...
We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform base ...
Institute of Electrical and Electronics Engineers2012
When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. More ...
Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...
The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the ...
Using multiple families of image features is a very efficient strategy to improve performance in object detection or recognition. However, such a strategy induces multiple challenges for machine learning methods, both from a computational and a statistical ...