Efficient distribution-free learning of probabilistic concepts
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Machine Learning
Machine Learning
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
Streamed learning: one-pass SVMs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Structured prediction by joint kernel support estimation
Machine Learning
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
ACM Transactions on Algorithms (TALG)
INFORMS Journal on Computing
New approximation algorithms for minimum enclosing convex shapes
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Activized learning: transforming passive to active with improved label complexity
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We study the problem of learning largemargin half-spaces in various settings using coresets and show that coresets are a widely applicable tool for large margin learning. A large margin coreset is a subset of the input data sufficient for approximating the true maximum margin solution. In this work, we provide a direct algorithm and analysis for constructing large margin coresets. We show various applications including a novel coreset based analysis of large margin active learning and a polynomial time (in the number of input data and the amount of noise) algorithm for agnostic learning in the presence of outlier noise. We also highlight a simple extension to multi-class classification problems and structured output learning.