Add to ORKGThis work shows how to estimate the support of the distribution of some data when observations in the data have uncertainties. To model uncertainties, we consider each observation of the training set to be a random vector distributed according to a distribution with first and second moments in a local vicinity. To estimate the support, we used the support vector data description method. Chance Constrain Approach • Let {xi ∼ (x̂i,Σi)}ni=1 be the training set, the probabilistic level
This work deals with the propagation of uncertainty in decision analysis problems. In the practice, ...
An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a p...
International audienceWe consider the binary classification problem when data are large and subject ...
We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classif...
—We address the problem of learning a data description model for a dataset whose elements or observa...
The central theme of the thesis is to study linear and non linear SVM formulations in the presence o...
This paper investigates a new learning model in which the input data is corrupted with noise. We pre...
International audienceWe address the problem of learning a data description model from a dataset con...
Support Vector Machines (SVM) is one of the most widely used technique in machines leaning. After th...
Abstract. In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain ...
Probabilistic and set-based methods are two approaches for model (in)validation, parameter and state...
In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain data are o...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
Uncertain data has been rapidly accumulated in many important applications, such as sensor networks,...
In uncertainty analysis, estimating the degree of uncertainty based on some physical experiments is ...
This work deals with the propagation of uncertainty in decision analysis problems. In the practice, ...
An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a p...
International audienceWe consider the binary classification problem when data are large and subject ...
We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classif...
—We address the problem of learning a data description model for a dataset whose elements or observa...
The central theme of the thesis is to study linear and non linear SVM formulations in the presence o...
This paper investigates a new learning model in which the input data is corrupted with noise. We pre...
International audienceWe address the problem of learning a data description model from a dataset con...
Support Vector Machines (SVM) is one of the most widely used technique in machines leaning. After th...
Abstract. In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain ...
Probabilistic and set-based methods are two approaches for model (in)validation, parameter and state...
In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain data are o...
we demonstrate several techniques to prove safety guarantees for robust control problems with statis...
Uncertain data has been rapidly accumulated in many important applications, such as sensor networks,...
In uncertainty analysis, estimating the degree of uncertainty based on some physical experiments is ...
This work deals with the propagation of uncertainty in decision analysis problems. In the practice, ...
An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a p...
International audienceWe consider the binary classification problem when data are large and subject ...