Dynamic programming algorithm optimization for spoken word recognition
Readings in speech recognition
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Connectivity Constraint using Bridges
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Length-lex ordering for set CSPs
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Solving set constraint satisfaction problems using ROBDDs
Journal of Artificial Intelligence Research
The steel mill slab design problem revisited
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
SMT-aided combinatorial materials discovery
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Solutions for hard and soft constraints using optimized probabilistic satisfiability
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Hi-index | 0.00 |
Motivated by an important and challenging task encountered in material discovery, we consider the problem of finding K basis patterns of numbers that jointly compose N observed patterns while enforcing additional spatial and scaling constraints. We propose a Constraint Programming (CP) model which captures the exact problem structure yet fails to scale in the presence of noisy data about the patterns. We alleviate this issue by employing Machine Learning (ML) techniques, namely kernel methods and clustering, to decompose the problem into smaller ones based on a global data-driven view, and then stitch the partial solutions together using a global CP model. Combining the complementary strengths of CP and ML techniques yields a more accurate and scalable method than the few found in the literature for this complex problem.