Concept

Learning vector quantization

Publications associées (11)

Data-Driven Convergence Prediction of Astrobots Swarms

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian, Francesco Basciani

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination\footnote{\z{A coordination sa ...

2021

SwarmLab: a MATLAB Drone Swarm Simulator

Dario Floreano, Fabrizio Schiano, Enrica Soria

Among the available solutions for drone swarm simulations, we identified a lack of simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis. Moreover, users who want to dive in the research field of drone swa ...

2020

Covering convex bodies and the Closest Vector Problem

Moritz Andreas Venzin, Márton Naszódi

We present faster algorithms for the approximate Closest Vector Problem under l_p norms ...

2019

Jump-Penalized Least Absolute Values Estimation of Scalar or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We study jump-penalized estimators based on least absolute deviations which are often referred to as Potts estimators. They are estimators for a parsimonious piecewise constant representation of noisy data having a noise distribution which has heavier tail ...

Oxford University Press2017

Implementing Neural Networks Efficiently

Ronan Collobert

Neural networks and machine learning algorithms in general require a flexible environment where new algorithm prototypes and experiments can be set up as quickly as possible with best possible computational performance. To that end, we provide a new framew ...

Springer2012

Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition

Hervé Bourlard, Afsaneh Asaei, Benjamin Picart

Class posterior distributions can be used to classify or as intermediate features, which can be further exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech recognition performance. In this paper we examine the p ...

Idiap2010

Improved Phone Posterior Estimation Through k-NN and MLP-Based Similarity

Benjamin Picart

In this work, we investigate the possible use of k-nearest neighbour (kNN) classifiers to perform frame-based acoustic phonetic classification, hence replacing Gaussian Mixture Models (GMM) or MultiLayer Perceptrons (MLP) used in standard Hidden Markov Mod ...

Idiap2009

SOM-based Clustering of Multilingual Documents Using an Ontology

Minh Hai Pham

Clustering similar documents is a difficult task for text data mining. Difficulties stem especially from the way documents are translated into numerical vectors. In this chapter, we will present a method that uses Self Organizing Map (SOM) to cluster medic ...

Information Science Reference2008

Current Mode Euclidean Distance Calculation Circuit for Kohonen's Neural Network Implemented in CMOS 0.18μm Technology

Rafal Tomasz Dlugosz

In this paper we present analog current mode Euclidean distance calculation (EDC) block, which calculates the distance between two current vectors. The proposed circuit is an important part of the CMOS-implemented Kohonen’s neural network (KNN) designed fo ...

2007

The Euclidean k-Distance Transformartion in Arbitrary Dimensions - a Separable Implementation

Olivier Cuisenaire

The signed k-distance transformation (k-DT) computes the k nearest prototypes from each location on a discrete regular grid within a given D dimensional volume. We propose a new k-DT algorithm that divides the problem into D 1-dimensional problems and comp ...

EUSIPCO2005

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