Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Feature discovery for problem solving systems
Feature discovery for problem solving systems
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Feature construction for game playing
Machines that learn to play games
Automatic feature selection in neuroevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Stochastic optimization for collision selection in high energy physics
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Combining finite learning automata with GSAT for the satisfiability problem
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
The field of high energy physics aims to discover the underlying structure of matter by searching for and studying exotic particles, such as the top quark and Higgs boson, produced in collisions at modern accelerators. Since such accelerators are extraordinarily expensive, extracting maximal information from the resulting data is essential. However, most accelerator events do not produce particles of interest, so making effective measurements requires event selection, in which events producing particles of interest (signal) are separated from events producing other particles (background). This article studies the use of machine learning to aid event selection. First, we apply supervised learning methods, which have succeeded previously in similar tasks. However, they are suboptimal in this case because they assume that the selector with the highest classification accuracy will yield the best final analysis; this is not true in practice, as such analyses are more sensitive to some backgrounds than others. Second, we present a new approach that uses stochastic optimization techniques to directly search for selectors that maximize either the precision of top quark mass measurements or the sensitivity to the presence of the Higgs boson. Empirical results confirm that stochastically optimized selectors result in substantially better analyses. We also describe a case study in which the best selector is applied to real data from the Fermilab Tevatron accelerator, resulting in the most precise top quark mass measurement of this type to date. Hence, this new approach to event selection has already contributed to our knowledge of the top quark's mass and our understanding of the larger questions upon which it sheds light.