Search Results for author: Amaury Lendasse

Found 10 papers, 0 papers with code

Mislabel Detection of Finnish Publication Ranks

no code implementations19 Dec 2019 Anton Akusok, Mirka Saarela, Tommi Kärkkäinen, Kaj-Mikael Björk, Amaury Lendasse

The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM).

Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices

no code implementations19 Dec 2019 Anton Akusok, Kaj-Mikael Björk, Leonardo Espinosa Leal, Yoan Miche, Renjie Hu, Amaury Lendasse

This concept paper highlights a recently opened opportunity for large scale analytical algorithms to be trained directly on edge devices.

Edge-computing

Extreme Learning Tree

no code implementations19 Dec 2019 Anton Akusok, Emil Eirola, Kaj-Mikael Björk, Amaury Lendasse

The paper proposes a new variant of a decision tree, called an Extreme Learning Tree.

Per-sample Prediction Intervals for Extreme Learning Machines

no code implementations19 Dec 2019 Anton Akusok, Yoan Miche, Kaj-Mikael Björk, Amaury Lendasse

Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall.

Prediction Intervals

A Web Page Classifier Library Based on Random Image Content Analysis Using Deep Learning

no code implementations18 Dec 2019 Leonardo Espinosa Leal, Kaj-Mikael Björk, Amaury Lendasse, Anton Akusok

The results show that the best method of classifying a webpage into the studies classes is to assign the class according to the maximum probability of any image belonging to this (weapon) class being above the threshold, across all the retrieved images.

General Classification Image Classification

Embedded Spectral Descriptors: Learning the point-wise correspondence metric via Siamese neural networks

no code implementations17 Oct 2017 Zhiyu Sun, Yusen He, Andrey Gritsenko, Amaury Lendasse, Stephen Baek

In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems.

HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection

no code implementations23 Sep 2014 Bo Han, Bo He, Tingting Sun, Mengmeng Ma, Amaury Lendasse

By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in.

Face Recognition Sparse Representation-based Classification

LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

no code implementations9 Aug 2014 Bo Han, Bo He, Rui Nian, Mengmeng Ma, Shujing Zhang, Minghui Li, Amaury Lendasse

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data.

RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

no code implementations9 Aug 2014 Bo Han, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, Amaury Lendasse

It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.

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