Add to ORKGWe apply methods of Statistical Mechanics to study the generalization performance of Support vector Machines in large data spaces
Generalization bounds depending on the margin of a classifier are a relatively recent development. T...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...
We present distribution independent bounds on the generalization misclassification performance of a ...
Abstract. Support vector machines (SVMs) and Boosting are possibly the two most popular learning app...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
We address the problem of binary linear classification with emphasis on algorithms that lead to sepa...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
The concept of large margins is a unifying principle for the analysis of many different approaches t...
The concept of large margins is a unifying principle for the analysis of many different approaches t...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
We address the problem of binary linear classification with emphasis on algorithms that lead to sepa...
Generalization bounds depending on the margin of a classifier are a relatively recent development. T...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...
We present distribution independent bounds on the generalization misclassification performance of a ...
Abstract. Support vector machines (SVMs) and Boosting are possibly the two most popular learning app...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
We address the problem of binary linear classification with emphasis on algorithms that lead to sepa...
Using methods of Statistical Physics, we investigate the generalization performance of support vecto...
The concept of large margins is a unifying principle for the analysis of many different approaches t...
The concept of large margins is a unifying principle for the analysis of many different approaches t...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
We address the problem of binary linear classification with emphasis on algorithms that lead to sepa...
Generalization bounds depending on the margin of a classifier are a relatively recent development. T...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...
Large margin classifiers have been shown to be very useful in many applications. The Support Vector ...