Search results for " classification"

Adding Real-Time Networking and QoS Capabilities to RTLinux-GPL

2006

This paper presents an architecture to build distributed embedded real-time systems in the RTLinux-GPL platform. The architecture (built in a layered fashion) has being built around open source projects ranging from Ethernet drivers to a CORBA environment. The paper focuses on those layers that give support for QoS and real-time networking over Ethernet networks. The main ideas are: to accomplish deterministic access times by using a TDMA protocol over Ethernet and to multiplex different types of traffic in that real-time network, providing different service types (QoS) to each type of traffic without jeopardizing the a priori guarantee of the system's real-time properties. Traffic types in…

On the complexity of the Saccharomyces bayanus taxon: Hybridization and potential hybrid speciation

2014

Although the genus Saccharomyces has been thoroughly studied, some species in the genus has not yet been accurately resolved; an example is S. bayanus, a taxon that includes genetically diverse lineages of pure and hybrid strains. This diversity makes the assignation and classification of strains belonging to this species unclear and controversial. They have been subdivided by some authors into two varieties (bayanus and uvarum), which have been raised to the species level by others. In this work, we evaluate the complexity of 46 different strains included in the S. bayanus taxon by means of PCR-RFLP analysis and by sequencing of 34 gene regions and one mitochondrial gene. Using the sequenc…

DEVELOPMENT AND IMPLEMENTATION OF MACHINE LEARNING METHODS FOR THE IIF IMAGES ANALYSIS

2021

Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network

2006

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters.…

Remote Sensing Image Classification with Large Scale Gaussian Processes

2017

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for…

Multi-label Methods for Prediction with Sequential Data

2017

The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data. From this study we draw upon the most suitable techniques from the area and develop two novel competitive approaches which can be applied to either kind of data. We carry out an empirical evaluation inves…

Human experts vs. machines in taxa recognition

2020

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hier…

A survey of active learning algorithms for supervised remote sensing image classification

2011

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active …

Randomized kernels for large scale Earth observation applications

2020

Abstract Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop st…

Modeling temporal treatment effects with zero inflated semi-parametric regression models: The case of local development policies in France

2017

International audience; A semi-parametric approach is proposed to estimate the variation along time of the effects of two distinct public policies that were devoted to boost rural development in France over a similar period of time. At a micro data level, it is often observed that the dependent variable, such as local employment, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a continuous response model. We introduce a conditional mixture model which combines a mass at zero and a continuous response. The suggested zero inflated semi-parametric statistical approach relies on the flexibility and modularity of additive models with the abi…