Concept

Scikit-learn

Related publications (27)

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

François Maréchal, Nils Christian Schüler, Sébastien Cajot

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice ...

2019

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Nadia Barbara Figueroa Fernandez

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level sk ...

EPFL2019

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Simultaneous and proportional control of a prosthetic hand and wrist is still a controversial issue, although giant steps have lately been made in this direction. In this paper, we study the application of a novel machine learning method to the problem, wi ...

2017

CARTHA / The Form of Form / Issue I / How to Learn Better

Dieter Dietz, Daniel Zamarbide Elizondo

CARTHA is a curated platform that focuses on sharing different forms of critical thinking regarding architecture and society. Through opinions, experiences and works, CARTHA aims to map out the contemporary architectural landscape. How to Learn Better inau ...

2016

Reinforcement learning: the effect of environment

Michael Herzog, He Xu

Reinforcement learning is a type of supervised learning, where reward is sparse and delayed. For example in chess, a series of moves is made until a sparse reward (win, loss) is issued, which makes it impossible to evaluate the value of a single move. Stil ...

2016

Weakly Supervised Object Segmentation with 004 dwaeConvolutional Neural Networks

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Can a machine learn how to segment different objects in real world images without having any prior knowledge about the delineation of the classes? In this paper, we demonstrate that this task is indeed possible. We address the problem by training a Convolu ...

Idiap2014

Multiclass Latent Locally Linear Support Vector Machines

Barbara Caputo, Marco Fornoni

Kernelized Support Vector Machines (SVM) have gained the status of o-the-shelf classiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grow ...

2013

Learning User's Confidence for Active Learning

Devis Tuia

In this paper, we study the applicability of active learning (AL) in operative scenarios. More particularly, we consider the well-known contradiction between the AL heuristics, which rank the pixels according to their uncertainty, and the user's confidence ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Clustering via diffusion adaptation over networks

Ali H. Sayed

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when all agents share the same objective or belong to the same group. However, if agents belong to different clusters o ...

IEEE2012

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