Concept

Ensemble learning

Related publications (493)

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Distributed learning via Diffusion adaptation with application to ensemble learning.

Ali H. Sayed

We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy cons ...

2012

Multivariate Boosting with Look-Up Tables for Face Processing

Cosmin Atanasoaei

This thesis proposes a novel unified boosting framework. We apply this framework to the several face processing tasks, face detection, facial feature localisation, and pose classification, and use the same boosting algorithm and the same pool of features ( ...

EPFL2012

Remote sensing image segmentation by active queries

Devis Tuia

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained ...

2012

A Real-Time Deformable Detector

Pascal Fua, François Fleuret, Karim Ali, David Hasler

We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform base ...

Institute of Electrical and Electronics Engineers2012

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to work ...

2012

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

2012

Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

Perceptual Learning, Roving, and Synaptic Drift

Michael Herzog, Aaron Michael Clarke

Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...

2012

Detection and Classification of Postural Transitions in Real-World Conditions

Kamiar Aminian, Anisoara Ionescu, Raluca Lidia Ganea

This study proposes a new robust classifier for sit-to-stand (SiSt) and stand-to-sit (StSt) detection in daily activity. The monitoring system consists of a single inertial sensor placed on the trunk. By using dynamic time warping, the trunk acceleration p ...

Institute of Electrical and Electronics Engineers2012

Data-Driven Enhancement of State Mapping-Based Cross-Lingual Speaker Adaptation

Hui Liang

The thesis work was motivated by the goal of developing personalized speech-to-speech translation and focused on one of its key component techniques – cross-lingual speaker adaptation for text-to-speech synthesis. A personalized speech-to-speech translator ...

EPFL2012