Add to ORKGTraining data: sample drawn i.i.d. from set according to some distribution , Problem: find hypothesis in (classifier) with small generalization error
Statistical learning theorycombines empirical risk and generalization functionin single optimized ob...
Recent theoretical results have shown that im-proved bounds on generalization error of clas-siers ca...
on why large margins are good for learning. Kernels and general similarity functions. L1 – L2 connec...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
We present a bound on the generalisation error of linear classifiers in terms of a refined margin qu...
An optimization problem Objective: Maximizing the margin of the linear classifier 3 Classifier Margi...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The pro...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
Ce rapport technique NeuroCOLT2, NC2-TR-1999-051-R, publie en décembre 1999, est paru dans une versi...
Editor: Much attention has been paid to the theoretical explanation of the empirical success of AdaB...
Random projections have been applied in many machine learning algorithms. However, whether margin is...
Statistical learning theorycombines empirical risk and generalization functionin single optimized ob...
Recent theoretical results have shown that im-proved bounds on generalization error of clas-siers ca...
on why large margins are good for learning. Kernels and general similarity functions. L1 – L2 connec...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
We present a bound on the generalisation error of linear classifiers in terms of a refined margin qu...
An optimization problem Objective: Maximizing the margin of the linear classifier 3 Classifier Margi...
In this paper we propose a new learning algorithm for kernel classifiers. Former approaches like Qua...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The pro...
A number of results have bounded generalization of a classier in terms of its margin on the training...
Typical bounds on generalization of Support Vector Machines are based on the minimum distance betwee...
Ce rapport technique NeuroCOLT2, NC2-TR-1999-051-R, publie en décembre 1999, est paru dans une versi...
Editor: Much attention has been paid to the theoretical explanation of the empirical success of AdaB...
Random projections have been applied in many machine learning algorithms. However, whether margin is...
Statistical learning theorycombines empirical risk and generalization functionin single optimized ob...
Recent theoretical results have shown that im-proved bounds on generalization error of clas-siers ca...
on why large margins are good for learning. Kernels and general similarity functions. L1 – L2 connec...