Regularized multi--task learning
- T. Evgeniou, M. Pontil
- Computer ScienceKnowledge Discovery and Data Mining
- 22 August 2004
An approach to multi--task learning based on the minimization of regularization functionals similar to existing ones, such as the one for Support Vector Machines, that have been successfully used in the past for single-- task learning is presented.
Convex multi-task feature learning
- Andreas Argyriou, T. Evgeniou, M. Pontil
- Computer ScienceMachine-mediated learning
- 1 December 2008
It is proved that the method for learning sparse representations shared across multiple tasks is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution.
Multi-Task Feature Learning
- Andreas Argyriou, T. Evgeniou, M. Pontil
- Computer ScienceNIPS
- 4 December 2006
The method builds upon the well-known 1-norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks, and develops an iterative algorithm for solving it.
Learning Multiple Tasks with Kernel Methods
- T. Evgeniou, C. Micchelli, M. Pontil
- Computer ScienceJournal of machine learning research
- 1 December 2005
The experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.
Regularization Networks and Support Vector Machines
- T. Evgeniou, M. Pontil, T. Poggio
- Computer ScienceAdvances in Computational Mathematics
- 1 April 2000
Both formulations of regularization and Support Vector Machines are reviewed in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.
Support Vector Machines: Theory and Applications
- T. Evgeniou, M. Pontil
- BiologyMachine Learning and Its Applications
- 27 September 2001
The goal of the chapter is to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
- F. Bach, J. Abernethy, Jean-Philippe Vert, T. Evgeniou
- Computer ScienceJournal of machine learning research
- 11 February 2008
This work presents a general approach for collaborative filtering using spectral regularization to learn linear operators mapping a set of "users" to aSet of possibly desired " objects", and provides novel representer theorems that are used to develop new estimation methods.
Stability of Randomized Learning Algorithms
- A. Elisseeff, T. Evgeniou, M. Pontil
- Computer Science, MathematicsJournal of machine learning research
- 1 December 2005
The formal definitions of stability for randomized algorithms are given and non-asymptotic bounds on the difference between the empirical and expected error as well as the leave-one-out and expectederror of such algorithms that depend on their random stability are proved.
A TRAINABLE PEDESTRIAN DETECTION SYSTEM
- Constantine Papgeorgiou, T. Evgeniou, T. Poggio
- Computer Science
- 1998
A trainable object detection system that automatically learns to detect objects of a certain class in unconstrained scenes and learns the pedestrian model from examples and uses no motion cues.
Generalized robust conjoint estimation
- T. Evgeniou, C. Boussios, G. Zacharia
- Computer Science
- 1 August 2005
A method for estimating preference models that can be highly nonlinear and robust to noise and is based on computationally efficient optimization techniques, which can be useful for analyzing large amounts of data that are noisy or for estimating interactions among product features.
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