Combining Discriminant Models with New Multi-Class SVMs

Guermeur, Yann

doi:10.1007/s100440200015

Cited by 86 publications

(66 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The multiclass SVM gave lower accuracy rates than the common methods. However, multi-class SVMs gave promising results and outperformed other combinatory methods in the prediction of protein secondary structures [15].…”

Section: Multi-class Svmmentioning

confidence: 99%

A trainable feature extractor for handwritten digit recognition

Lauer

Suen

Bloch

2007

Pattern Recognition

260

105

View full text Add to dashboard Cite

This article focusses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by Support Vector Machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples are generated by affine transformations and elastic distortions. Experiments are performed on the well known MNIST database to validate the method and the results show that the system can outperfom both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations.

show abstract

Section: Multi-class Svmmentioning

confidence: 99%

A trainable feature extractor for handwritten digit recognition

Lauer

Suen

Bloch

2007

Pattern Recognition

260

105

View full text Add to dashboard Cite

show abstract

“…Best generalization performance for KNN algorithm is given for K equal to 3. For SVMs, the multiclass implementation of [27] was used. Generalization performances were estimated using again a ten-fold cross validation approach.…”

Section: A Classification Resultsmentioning

confidence: 99%

Fast and robust gas identification system using an integrated gas sensor technology and Gaussian mixture models

et al. 2005

View full text Add to dashboard Cite

Abstract-Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. This paper shows that the combination of an integrated sensor array and a Gaussian mixture model permits success in gas identification problems. An integrated sensor array has been designed with the aim of combustion gases identification. Our identification system is able to quickly recognize gases with more than 96% accuracy. Robust detection is introduced through a drift counteraction approach based on extending the training data set using a simulated drift.

show abstract

“…Although they operate on the same class of functions and their learning problems all extend in a straightforward way the one of bi-class SVMs (precisely the 1-norm and the 2-norm ones), they exhibit distinct properties. In recent years, several comparative studies between M-SVMs and decomposition methods have been published (see for instance Guermeur, 2002;Hsu and Lin, 2002). In short, they establish that in practice, no model is uniformly superior or inferior to the others with respect to the standard criteria: prediction accuracy, sparsity, computational complexity, etc.…”

Section: During the Last Decade Many M-svms And Decomposition Methodmentioning

confidence: 99%