Concept

Kernel method

Related publications (230)

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Deep Learning via Semi-Supervised Embedding

Ronan Collobert

We show how nonlinear embedding algorithms popular for use with "shallow" semi-supervised learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of t ...

Springer2012

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Jean-Philippe Thiran, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise ...

Ieee2012

Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Michaël Unser, Daniel Sage, Kunal Narayan Chaudhury

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithm ...

IEEE2011

Multispectral SIFT for Scene Category Recognition

Sabine Süsstrunk, Matthew Brown

We use a simple modification to a conventional SLR camera to capture images of several hundred scenes in colour (RGB) and near-infrared (NIR). We show that the addition of near-infrared information leads to significantly improved performance in a scene-rec ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Palm Vein Recognition with Local Binary Patterns and Local Derivative Patterns

Leila Mirmohamadsadeghi, Andrzej Drygajlo

Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Pa ...

2011

Example-based Support Vector Machine for Drug Concentration Analysis

Giovanni De Micheli, Wenqi You Dubout

Machine learning has been largely applied to ana- lyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Goal-Based Caustics

Wenzel Alban Jakob, Marios Papas

We propose a novel system for designing and manufacturing surfaces that produce desired caustic images when illuminated by a light source. Our system is based on a nonnegative image decomposition using a set of possibly overlapping anisotropic Gaussian ker ...

2011

Progressive Expectation-maximization for Hierarchical Volumetric Photon Mapping

Wenzel Alban Jakob

State-of-the-art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel-based approaches is the excessive number of photons that a ...

Eurographics Association2011

Steerable Pyramids and Tight Wavelet Frames in L_2(R^d)

Michaël Unser, Dimitri Nestor Alice Van De Ville

We present a functional framework for the design of tight steerable wavelet frames in any number of dimensions. The 2-D version of the method can be viewed as a generalization of Simoncelli's steerable pyramid that gives access to a larger palette of steer ...

Institute of Electrical and Electronics Engineers2011

Comparison-Based Optimizers Need Comparison-Based Surrogates

Ilya Loshchilov

Taking inspiration from approximate ranking, this paper nvestigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. ...

2010