Add to ORKGIn this paper we investigate connections between statistical learning theory and data compression on the basis of support vector machine (SVM) model selection. Inspired by several generalization bounds we construct ``compression coefficients'' for SVMs, which measure the amount by which the training labels can be compressed by some classification hypothesis. The main idea is to relate the coding precision of this hypothesis to the width of the margin of the SVM. The compression coefficients connect well known quantities such as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel matrix and the number of support vectors. To test whether they are useful in practice we ran model selection experiments on several real world datasets...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
Dans le domaine de la classification, les algorithmes d'apprentissage par compression d'échantillon...
When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimat...
In this paper we investigate connections between statistical learning theory and data compression on...
In this paper we investigate connections between statistical learning theory and data compression on...
This dissertation covers two topics. First, the Compressed Learning with hard SVM. We characterize t...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
This thesis studies the generalization behavior of algorithms in Sample Compression Settings. It ext...
The support vector machine (SVM) algorithm is well known to the computer learning community for its ...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
Support Vector Machines (SVM) were developed by Vapnik [1] to solve the classification prob-lem, but...
A crucial issue in designing learning machines is to select the correct model parameters. When the n...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
Thesis (Ph.D. (Computer Engineering))--North-West University, Potchefstroom Campus, 2012As digital c...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
Dans le domaine de la classification, les algorithmes d'apprentissage par compression d'échantillon...
When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimat...
In this paper we investigate connections between statistical learning theory and data compression on...
In this paper we investigate connections between statistical learning theory and data compression on...
This dissertation covers two topics. First, the Compressed Learning with hard SVM. We characterize t...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
This thesis studies the generalization behavior of algorithms in Sample Compression Settings. It ext...
The support vector machine (SVM) algorithm is well known to the computer learning community for its ...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
Support Vector Machines (SVM) were developed by Vapnik [1] to solve the classification prob-lem, but...
A crucial issue in designing learning machines is to select the correct model parameters. When the n...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
Thesis (Ph.D. (Computer Engineering))--North-West University, Potchefstroom Campus, 2012As digital c...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
Dans le domaine de la classification, les algorithmes d'apprentissage par compression d'échantillon...
When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimat...