Search results for "Machine"

Monolingual and cross-lingual intent detection without training data in target languages

2021

Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language

Dimensional control of metallic objects by artificial vision: contribution to lighting condition studies

1999

We present an original method that enables real time automatic dimensional control of defects on metallic objects. The method is based on the theoretical and experimental study of light reflection mechanisms that can be seen as a function of the surface roughness and of the light source size. We show that it is possible to reconstruct the profile of an object from a single gray level image. An industrial application enables us to validate our results. Finally, an error computation fixes the limits of our method and enables us to better understand illumination problems.

Estimating the gravity equation with the actual number of exporting firms

2013

Para estimar correctamente el efecto de los costes de comerciar sobre las exportaciones de las empresas, la ecuación de gravedad debe controlar por el número de empresas que opera en el mercado internacional. Debido a la ausencia de datos, estudios anteriores han controlado esta variable mediante técnicas econométricas que también pueden generar estimaciones sesgadas. Para superar estos problemas este trabajo estima una ecuación de gravedad utilizando una nueva base de datos de la OCDE y EUROSTAT , que incluye el número de empresas exportadoras en cada relación bilateral. Nuestros resultados muestran que no controlar el margen extensivo genera sesgos muy importantes en la estimación de los …

Dynamic Preisach Hysteresis Model for Magnetostrictive Materials for Energy Application

2013

Quantification and classification of high-resolution magic angle spinning data for brain tumor diagnosis.

2007

The goal of this work is to propose a complete protocol (preprocessing, processing and classification) for classifying brain tumors with proton high-resolution magic-angle spinning ((1)H HR-MAS) data. The different steps of the procedure are detailed and discussed. Feature extraction techniques such as peak integration, including also the automated quantitation method AQSES, were combined with linear (LDA) and non-linear (least-squares support vector machine or LS-SVM) classifiers. Classification accuracy was assessed using a stratified random sampling scheme. The results suggest that LS-SVM performs better than LDA while AQSES performs better than the standard peak integration feature extr…

Hyperspectral detection of citrus damage with Mahalanobis kernel classifier

2007

Presented is a full computer vision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.

Decision Committee Learning with Dynamic Integration of Classifiers

2000

Decision committee learning has demonstrated spectacular success in reducing classification error from learned classifiers. These techniques develop a classifier in the form of a committee of subsidiary classifiers. The combination of outputs is usually performed by majority vote. Voting, however, has a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then the average classifier will give a wrong prediction, and the majority vote will more probably result in a wrong prediction. Instead of voting, dynamic integration of classifiers can be used, which is based on the assumption that each committee member is best inside certain subar…

Dynamic Integration of Decision Committees

2000

Decision committee learning has demonstrated outstanding success in reducing classification error with an ensemble of classifiers. In a way a decision committee is a classifier formed upon an ensemble of subsidiary classifiers. Voting, which is commonly used to produce the final decision of committees has, however, a shortcoming. It is unable to take into account local expertise. When a new instance is difficult to classify, then it easily happens that only the minority of the classifiers will succeed, and the majority voting will quite probably result in a wrong classification. We suggest that dynamic integration of classifiers is used instead of majority voting in decision committees. Our…

Dynamic integration with random forests

2006

Random Forests (RF) are a successful ensemble prediction technique that uses majority voting or averaging as a combination function. However, it is clear that each tree in a random forest may have a different contribution in processing a certain instance. In this paper, we demonstrate that the prediction performance of RF may still be improved in some domains by replacing the combination function with dynamic integration, which is based on local performance estimates. Our experiments also demonstrate that the RF Intrinsic Similarity is better than the commonly used Heterogeneous Euclidean/Overlap Metric in finding a neighbourhood for local estimates in the context of dynamic integration of …

Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging

2018

Alzheimer’s disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different hy…