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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

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Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Predicting the Dominant Clique in Meetings through Fusion of Nonverbal Cues

Daniel Gatica-Perez, Dinesh Babu Jayagopi

This paper addresses the problem of automatically predicting the dominant clique (i.e., the set of K-dominant people) in face-to-face small group meetings recorded by multiple audio and video sensors. For this goal, we present a framework that integrates a ...

2008

Predicting the dominant clique in meetings through fusion of nonverbal cues

Daniel Gatica-Perez, Dinesh Babu Jayagopi

This paper addresses the novel problem of automatically predicting the dominant clique (i.e., the set of K-dominant people) in face-to-face small group meetings recorded by multiple audio and video sensors. For this goal, we present a framework that integr ...

IDIAP2008

Ensembles for sequence learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

EPFL2007

Ensembles for Sequence Learning

Christos Dimitrakakis

École Polytechnique Fédérale de Lausanne2006

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

A Taxonomy for Semi-Supervised Learning Methods

Matthias Seeger

We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature. Finally, we shed more detailed ...

MIT Press2006

A Multitask Learning Approach to Document Representation using Unlabeled Data

Samy Bengio, Mikaela Keller

Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method tha ...

IDIAP2006

Semi-supervised Adapted HMMs for Unusual Event Detection

Daniel Gatica-Perez, Samy Bengio, Dong Zhang

We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supe ...

2005