Concept

Linear separability

Publications associées (31)

Dictionary learning for fast classification based on soft-thresholding

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM

Devis Tuia

Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Single trial analysis of slow cortical potentials: A study on anticipation related potentials

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Gangadhar Garipelli

Objective. Abundant literature suggests the use of Slow Cortical Potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to low signal to noise ratio, often these potentials are studied using grand-average analysis, which conceal ...

Institute of Physics2013

Separable Filter Learning with Tensor Decomposition

Bugra Tekin

Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally de- manding and impractical to us ...

2013

Learning Separable Filters with Shared Parts

Bugra Tekin

2013

Learning from Images with Captions Using the Maximum Margin Set Algorithm

Barbara Caputo, Jie Luo, Vittorio Ferrari

A large amount of images with accompanying text captions are available on the Internet. These are valuable for training visual classifiers without any explicit manual intervention. In this paper, we present a general framework to address this problem. Unde ...

Idiap2011

Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Joel Praveen Pinto

In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in ...

EPFL2010

Semantic coding by supervised dimensionality reduction

Pascal Frossard, Effrosyni Kokiopoulou

This paper addresses the problem of representing multimedia information under a compressed form that permits efficient classification. The semantic coding problem starts from a subspace method where dimensionality reduction is formulated as a matrix factor ...

2008

Cue Integration for Medical Image Annotation

Barbara Caputo, Tatiana Tommasi

This paper presents the algorithms and results of our par- ticipation to the image annotation task of ImageCLEFmed 2007. We proposed a multi-cue approach where images are represented both by global and local descriptors. These cues are combined following t ...

Springer-Verlag2008

Discrmininant Models for Text-independent Speaker Verification

This thesis addresses text-independent speaker verification from a machine learning point of view. We use the machine learning framework to better define the problem and to develop new unbiased performance measures and statistical tests to compare objectiv ...

IDIAP2006

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