Concept

Evaluation of binary classifiers

Publications associées (31)

Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Ping-Keng Jao

Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variabilit ...

2018

Efﬁcient Feature Embeddings for Student Classiﬁcation with Variational Auto-encoders

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high ...

EDM2017

BDD Minimization for Approximate Computing

Mathias Soeken, Arun Chandrasekharan

We present Approximate BDD Minimization (ABM) as a problem that has application in approximate computing. Given a BDD representation of a multi-output Boolean function, ABM asks whether there exists another function that has a smaller BDD representa ...

IEEE2016

A Fully-Integrated IC with 0.85-uW/Channel Consumption for Epileptic iEEG Detection

Alexandre Schmid, Mahsa Shoaran

Feature extraction from a multichannel compressed neural signal is introduced in this brief. Compressive sensing (CS) is an efficient method for reducing the transmission data rate of sparse biological signals and lowering the power consumption of resource ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Scaled False Discovery Proportion and Related Error Metrics

Jean-Philippe Thiran, Stephan Morgenthaler, Djalel Eddine Meskaldji

In multiple testing, a variety of control metrics of false positives have been introduced such as the Per Family Error Rate (PFER), Family-Wise Error Rate (FWER), the False Discovery Rate (FDR), the False Exceedence Rate (FER). In this talk, we present a c ...

2013

Learning Pattern Transformation Manifolds for Classification

Pascal Frossard, Elif Vural

Manifold models provide low-dimensional representations that are useful for analyzing and classifying data in a transformation-invariant way. In this paper we study the problem of jointly building multiple pattern transformation manifolds from a collection ...

2012

Selection and Aggregation of Ranking Criteria for Retrieval from Scientific Publication Databases

Martin Veselý

Selection and aggregation of ranking criteria became an important topic in information retrieval as search is getting more specialized and as volume of electronically available information grows. In this context, document ranking has undergone a shift from ...

EPFL2012

Thick Boundaries in Binary Space and Their Influence on Nearest-Neighbor Search

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski

Binary descriptors allow faster similarity computation than real-valued ones while requiring much less storage. As a result, many algorithms have recently been proposed to binarize ﬂoating-point descriptors so that they can be searched for quickly. Unfortu ...

Elsevier2012

A comparison of different automated methods for the detection of white matter lesions in MRI data

Ahmed Abdulkadir

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. T ...

Elsevier2011

GeneNetWeaver 3.0: realistic benchmark generation and performance profiling of network inference methods

Dario Floreano, Daniel Marbach, Thomas Schaffter

Numerous methods have been developed for inference of gene regulatory networks from expression data, however, their strengths and weaknesses remain poorly understood. Accurate and systematic evaluation of these methods is hampered by the difficulty of cons ...

2010

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