On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Interior-Point Methods for Massive Support Vector Machines
SIAM Journal on Optimization
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Convex Optimization
Linux Journal
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
A sequential dual method for large scale multi-class linear svms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
P-packSVM: Parallel Primal grAdient desCent Kernel SVM
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Proceedings of the 19th international conference on World wide web
Large linear classification when data cannot fit in memory
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Selective block minimization for faster convergence of limited memory large-scale linear models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
| Hi-index | 0.00 |
Recent advances in linear classification have shown that for applications such as document classification, the training process can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods cannot be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. Because data cannot fit in memory, many design considerations are very different from those for traditional algorithms. We discuss and compare with existing approaches that are able to handle data larger than memory. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method.