Person
Alexei Pozdnoukhov
This person is no longer with EPFL
Related publications (12)
Active learning for monitoring network optimization
Devis Tuia, Alexei Pozdnoukhov
This book provides an introduction to spatio-temporal design that contains a description of one or two basic settings (e.g., migration and biodiversity) that includes real data sets, data-generating mechanisms, and possible simulation scenarios. Furthermor ...
John Wiley & Sons2013Using active learning for monitoring networks design: the example of wind power plants sites evaluation
Devis Tuia, Alexei Pozdnoukhov
Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highl ...
2012Prior Knowledge in Kernel Methods
Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...
École Polytechnique Fédérale de Lausanne2006Prior knowledge in Kernel methods
Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...
EPFL2006A Kernel Classifier for Distributions
Samy Bengio, Alexei Pozdnoukhov
This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well- ...
IDIAP2005Invariances in Kernel Methods: From Samples to Objects
Samy Bengio, Alexei Pozdnoukhov
This paper presents a general method for incorporating prior knowledge into kernel methods such as Support Vector Machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assumi ...
IDIAP2004Tangent Vector Kernels for Invariant Image Classification with SVMs
Samy Bengio, Alexei Pozdnoukhov
This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invari ...
2004From Samples to Objects in Kernel Methods
Samy Bengio, Alexei Pozdnoukhov
This paper presents a general method for incorporating prior knowledge into kernel methods. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assuming that all points in the given ...
IDIAP2003Tangent Vector Kernels for Invariant Image Classification with SVMs
Samy Bengio, Alexei Pozdnoukhov
This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invari ...
IDIAP2003The analysis of kernel ridge regression learning algorithm.
The paper presents Kernel Ridge Regression, a nonlinear extension of the well known statistical model of ridge regression. New insights on the method are also presented. In particular, the connection between ridge regression and local translation-invariant ...
IDIAP2002