Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Information Systems (TOIS)
Query modification using genetic algorithms in vector space models
International Journal of Expert Systems
Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Crossover improvement for the genetic algorithm in information retrieval
Information Processing and Management: an International Journal
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A clustering strategy based on a formalism of the reproductive process in natural systems
SIGIR '79 Proceedings of the 2nd annual international ACM SIGIR conference on Information storage and retrieval: information implications into the eighties
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic algorithms in relevance feedback: a second test and new contributions
Information Processing and Management: an International Journal
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
ACM Transactions on Asian Language Information Processing (TALIP)
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
Information Processing and Management: an International Journal
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A hybrid system for dental milling parameters optimisation
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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This paper describes our experiments on automatic parameter optimization for the Japanese monolingual retrieval task. Unlike regression approaches, we optimized parameters completely independently of retrieval models enabling the optimized parameter set to illustrate the characteristics of the target test collections. We adopted genetic algorithms as optimization tools and cross-validated with four test collections, namely the CLIR-J-J collections for NTCIR-3 to NTCIR-6. The most difficult retrieval parameters to optimize are the feedback parameters, because there are no principles for calibrating them. Our approach optimized feedback parameters and basic scoring parameters at the same time. Using test sets and validation sets, we achieved effectiveness levels comparable with very strong baselines, i.e., the best-performing NTCIR official runs.