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Scene Recognition with Naive Bayes Non-linear Learning

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Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

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 Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Integrated calorimetric microreactor in low-temperature cofired ceramic (LTCC) technology

Peter Ryser, Thomas Maeder, Yannick Sébastien Fournier, Raphael Willigens

Object of this work 1) Explore the feasibility of an LTCC microreactor • Chemical stability: compatible with HCl, NaOH, ... • Thermal stability to >100°C 2) Achieve a high degree of integration • Temperature measurement • Reactant flow measurement • Temper ...

2006

Kernel Methods for Melanoma Recognition

Barbara Caputo, Tatiana Tommasi

Malignant melanoma is the most deadly for of skin lesion. Early diagnosis is of critical importance to patient survival. Visual recognition algorithms could potentially be of great help for physicians in a computer assisted diagnosis system. Previous work ...

2006

Detecting Intentional Mental Transitions in an Asynchronous BCI

José del Rocio Millán Ruiz, Pierre Ferrez

The inclusion of mental tasks transitions detection (MTTD) has proven a useful tool in guiding the transduction process of a BCI working under an asynchronous protocol. MTTD allows for the extraction of the signal's contextual information in order to infer ...

IDIAP2006

Multi-stream ASR: An Oracle Perspective

Hervé Bourlard, Hemant Misra

Multi-stream based automatic speech recognition (ASR) systems outperform their single stream counterparts, specially in case of noisy speech. The main issues in multi-stream systems are: a) Find the feature streams carrying complementary information, and b ...

2006

Kernel-based classifiers with applications to face detection

Vlad Popovici

Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...

EPFL2004

Boosting Pixel-based Classifiers for Face Verification

Sébastien Marcel, Yann Rodriguez

The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machi ...

Springer-Verlag2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...

2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

2004