C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Gaussian Mean-Shift Is an EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The Journal of Machine Learning Research
On the number of modes of a Gaussian mixture
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Selection intensity in cellular evolutionary algorithms for regular lattices
IEEE Transactions on Evolutionary Computation
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A spatial EA framework for parallelizing machine learning methods
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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The scalability of machine learning (ML) algorithms has become a key issue as the size of training datasets continues to increase. To address this issue in a reasonably general way, a parallel boosting algorithm has been developed that combines concepts from spatially structured evolutionary algorithms (SSEAs) and ML boosting techniques. To get more insight into the algorithm, a proper theoretical and empirical analysis is required. This paper is a first step in that direction. First, it establishes the connection between this algorithm and well known density estimation and mixture model approaches used by the machine learning community. The paper then analyzes the algorithm in terms of varioustheoretical and empirical properties such as convergence to large margins, scalability effects on accuracy and speed, robustness to noise, and connections to support vector machines in terms of instances converged to.