The Strength of Weak Learnability
Machine Learning
Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
Mathematical Programming: Series A and B
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Las Vegas algorithms for linear and integer programming when the dimension is small
Journal of the ACM (JACM)
Query size estimation by adaptive sampling
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
PALO: a probabilistic hill-climbing algorithm
Artificial Intelligence
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
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An adaptive version of the boost by majority algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Analysis and application of adaptive sampling
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A sequential sampling algorithm for a general class of utility criteria
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Optimal Algorithm for Monte Carlo Estimation
SIAM Journal on Computing
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Provably Fast Training Algorithms for Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Scaling Up a Boosting-Based Learner via Adaptive Sampling
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A Random Sampling Technique for Training Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Practical Algorithms for On-line Sampling
DS '98 Proceedings of the First International Conference on Discovery Science
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
DS '99 Proceedings of the Second International Conference on Discovery Science
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Optimization methods in massive data sets
Handbook of massive data sets
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Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields including computer science. Good softwares have been demanded for various knowledge discovery tasks. For such softwares, we often need to develop efficient algorithms for handling huge data sets. Random sampling is one of the important algorithmic methods for processing huge data sets. In this paper, we explain some random sampling techniques for speeding up learning algorithms and making them applicable to large data sets [15,16,4,3]. We also show some algorithms obtained by using these techniques.